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PATH HOLE DETECTION- A Modern Pothole Detection Technique Using Deep Learning

Road Accident Detection And Avoidance Are A More Difficult And Challenging Problem In India As Poor Quality Of Construction Materials Get Used In Road Drainage System Construction. Due To The Above Problems, Roads Get Damaged Early And Potholes Appear On The Roads Which Cause Accidents. According To A Report Submitted By The Ministry Of Road Transport And Highways Transport Research Wing New Delhi In 2017, Approximately 4,64,910 Accidents Happen Per Year In India. This Paper Proposed A Deep Learning-based Model That Can Detect Potholes Early Using Images And Videos Which Can Reduce The Chances Of An Accident. This Model Is Basically Based On Transfer Learning, Faster Region-based Convolutional Neural Network(F-RCNN) And Inception-V2. There Are Many Models For Pothole Detection That Uses The Accelerometer (without Using Images And Videos) With Machine Learning Techniques, But A Less Number Of Pothole Detection Models Can Be Found Which Uses Only Machine Learning Techniques To Detect Potholes. The Results Of This Work Have Shown That Our Proposed Model Outperforms Other Existing Techniques Of Potholes Detection.

IMAGE ENHANCEMENT- An Effectual Underwater Image Enhancement Using Deep Learning Algorithm

Digital Image Processing Domain Is Growing Day-by-day By Introducing Novel Technologies To Provide Assistance For Several Applications Such As Robotic Activities, Underwater Network Formation, And So On. In Particular, Underwater Image Processing Is Considered As The Crucial Task In Image Processing Industry Due To The Flow Of Light Waves That Are Not In The Specific And Expected Range Under The Water Level. While Image Restoration Technology Can Adequately Consider Removing This Same Haze From Source Images, They Need To Obtain Several Images From A Certain Place That Prevent It From Being Used In A Real-time System. To Overcome This Issue, A Deep Study Approach Is Developed By Providing Excellent Outcomes Of Deep Learning Approaches In Several Other Image Analysis Concerns Such As Colorizing Images Or Object Identification. A Convolution Neural Network (CNN) Model Is Trained To De-haze The Individual Images With Image Restoration In Order To Perform Further With An Image Improvement. The Proposed Approach Can Produce Images With Image Restoration Quality By Including A Standard Image Input And Here, The Neural Network Is Evaluated By Using Images And Features, Which Are Obtained From Separate Areas To Prove Its Capacity To Generalize. The Efficiency Of The Proposed Approach Is High When Compared To Other Existing Methods.

PLANT DISEASE DETECTION USING CNN- Disease Detection Of Plant Leaf Using Image Processing And CNN With Preventive Measures Flask Web Framework

One Of The Important And Tedious Task In Agricultural Practices Is The Detection Of The Disease On Crops. It Requires Huge Time As Well As Skilled Labor. This Paper Proposes A Smart And Efficient Technique For Detection Of Crop Disease Which Uses Computer Vision And Machine Learning Techniques. The Proposed System Is Able To Detect 20 Different Diseases Of 5 Common Plants With 93% Accuracy.

DOG BREED CLASSIFICATION- Breakthrough Conventional Based Approach For Dog Breed Classification Using CNN With Transfer Learning

Dogs Are One Of The Most Common Domestic Animals. Due To A Large Number Of Dogs, There Are Several Issues Such As Population Control, Decrease Outbreak Such As Rabies, Vaccination Control, And Legal Ownership. At Present, There Are Over 180 Dog Breeds. Each Dog Breed Has Specific Characteristics And Health Conditions. In Order To Provide Appropriate Treatments And Training, It Is Essential To Identify Individuals And Their Breeds. The Paper Presents The Classification Methods For Dog Breed Classification Using Two Image Processing Approaches 1) Conventional Based Approaches By Local Binary Pattern (LBP) And Histogram Of Oriented Gradient (HOG) 2) The Deep Learning Based Approach By Using Convolutional Neural Networks (CNN) With Transfer Learning. The Result Shows That Our Retrained CNN Model Performs Better In Classifying A Dog Breeds. It Achieves 96.75% Accuracy Compared With 79.25% Using The HOG Descriptor.

PLANT LEAF DISEASE RECOGNISATION- Leaf Disease Detection And Classification Based On Machine Learning

Detection Of Diseases In Plants Is A Significant Task That Has To Be Done In Agriculture. This Is Something On Which The Economy Profoundly Depends. Infection Discovery In Plants Is A Significant Job In The Agribusiness Field, As Having Diseases In Plants Is Very Common. To Recognize The Diseases In Leaves, A Continuous Observation Of The Plants Is Required. This Observation Or Continuous Monitoring Of The Plants Takes A Lot Of Human Effort And It Is Time-consuming Too. To Make It Simply Some Sort Of Programmed Strategy Is Required To Observe The Plants. Program Based Identification Of Diseases In Plants Makes Easier To Detect The Damaged Leaves And Reduces Human Efforts And Time-saving. The Proposed Algorithm Distinguishing Sickness In Plants And Classify Them More Accurately As Compared To Existing Techniques.

SCRATCH REMOVAL OF IMAGE FROM OLD IMAGE- Research On Repairing Historical Photos Of Damaged Scratches Based On Computer Technology

Historical Photographs Record The True Face Of A Moment In The Development Of Human History, They Have Authenticity, Vividness, And Unique Values. However, Due To Various Factors, Aging And Damage Will Occur. With The Development Of Computer Technology, The Restoration Technology Is More Used In Photo Restoration And Virtual Restoration Of Cultural Relics. This Paper First Analyzes The Principle Of Repairing Photo Archives Based On Computer Technology, And Then Uses The Combination Of Statistics And Computer Image Processing Technology To Detect And Repair The Scratches In Historical Photographs. And The Paper Establishes A Model Repair Framework, Which Provides A New Idea For The Repair Of Such Historical Photos. The Experimental Results Show That The Method Has A Significant Repair Effect.

SKIN CANCER CLASSIFICATION- Skin Cancer Classification Using Image Processing And Machine Learning

One Of The Most Rapidly Spreading Cancers Among Various Other Types Of Cancers Known To Humans Is Skin Cancer. Melanoma Is The Worst And The Most Dangerous Type Of Skin Cancer That Appears Usually On The Skin Surface And Then Extends Deeper Into The Layers Of Skin. However, If Diagnosed At An Early Stage; The Survival Rate Of Melanoma Patients Is 96% With Simple And Economical Treatments. The Conventional Method Of Diagnosing Melanoma Involves Expert Dermatologists, Equipment, And Biopsies. To Avoid The Expensive Diagnosis, And To Assist Dermatologists, The Field Of Machine Learning Has Proven To Provide State Of The Art Solutions For Skin Cancer Detection At An Earlier Stage With High Accuracy. In This Paper, A Method For Skin Lesion Classification And Segmentation As Benign Or Malignant Is Proposed Using Image Processing And Machine Learning. A Novel Method Of Contrast Stretching Of Dermoscopic Images Based On The Methods Of Mean Values And Standard Deviation Of Pixels Is Proposed. Then The OTSU Thresholding Algorithm Is Applied For Image Segmentation. After The Segmentation, Features Including Gray Level Co-occurrence Matrix (GLCM) Features For Texture Identification, The Histogram Of Oriented Gradients (HOG) Object, And Color Identification Features Are Extracted From The Segmented Images. Principal Component Analysis (PCA) Reduction Of HOG Features Is Performed For Dimensionality Reduction. Synthetic Minority Oversampling Technique (SMOTE) Sampling Is Performed To Deal With The Class Imbalance Problem. The Feature Vector Is Then Standardized And Scaled. A Novel Approach Of Feature Selection Based On The Wrapper Method Is Proposed Before Classification. Classifiers Including Quadratic Discriminant, SVM (Medium Gaussian), And Random Forest Are Used For Classification. The Proposed Approach Is Verified On The Publicly Accessible Dataset Of ISIC-ISBI 2016. Maximum Accuracy Is Achieved Using The Random Forest Classifier. The Classification Accuracy Of The Proposed System With The Random Forest Classifier On ISIC-ISBI 2016 Is 93.89%. The Proposed Approach Of Contrast Stretching Before The Segmentation Gives Satisfactory Results Of Segmentation. Further, The Proposed Wrapper-based Approach Of Feature Selection In Combination With The Random Forest Classifier Gives Promising Results As Compared To Other Commonly Used Classifiers.

SKIN CANCER DETECTION USING FLASK API- A Smartphone Based Application For Skin Cancer Classification Using Deep Learning With Clinical Images And Lesion Information

Over The Last Decades, The Incidence Of Skin Cancer, Melanoma And Non-melanoma, Has Increased At A Continuous Rate. In Particular For Melanoma, The Deadliest Type Of Skin Cancer, Early Detection Is Important To Increase Patient Prognosis. Recently, Deep Neural Networks (DNNs) Have Become Viable To Deal With Skin Cancer Detection. In This Work, We Present A Smartphone-based Application To Assist On Skin Cancer Detection. This Application Is Based On A Convolutional Neural Network(CNN) Trained On Clinical Images And Patients Demographics, Both Collected From Smartphones. Also, As Skin Cancer Datasets Are Imbalanced, We Present An Approach, Based On The Mutation Operator Of Differential Evolution (DE) Algorithm, To Balance Data. In This Sense, Beyond Provides A Flexible Tool To Assist Doctors On Skin Cancer Screening Phase, The Method Obtains Promising Results With A Balanced Accuracy Of 85% And A Recall Of 96%.

ACTIVITY RECOGNITION OF THE SPORT PERSON- Computer Vision-based Survey On Human Activity Recognition System Challenges And Applications

For Surveillance Purpose, Lots Of Method Were Used By The Researchers But Computer Vision Based Human Activity Recognition (HAR) Technologies/systems Received The Most Interest Because They Automatically Distinguish Human Behaviour And Movements From Video Data Utilizing Recorded Details From Cameras. But The Extraction Of Accurate And Opportune Information From Video Of Human's Activities And Behaviours Is Most Important And Difficult Task In Pervasive Computing Environment. Due To Lots Of Applications Of HAR Systems Like In Medical Field, Security, Visual Monitoring, Video Recovery, Entertainment And Irregular Behaviour Detection, The Accuracy Of System Is Most Important Factors For Researchers. This Review Article Presents A Brief Survey Of The Existing Video Or Vision-based HAR System To Find Out Their Challenges And Applications In Three Aspects Such As Recognition Of Activities, Activity Analysis, And Decision From Visual Content Representation. In Many Applications, System Recognition Time And Accuracy Is Most Important Factor And It Is Affected Due To An Increase In The Usage Of Simple Or Low Quality Type Cameras For Automated Systems. So, To Obtain A Better Accuracy And Fast Responses, The Usage Of Demanding And Computationally Intelligent Classification Techniques Such As Deep Learning And Machine Learning Is A Better Option For Researchers. In This Survey, We Addressed Numerous Computationally Intelligent Classification Techniques-based Research For HAR From 2010 To 2020 For A Better Analysis Of The Benefits And Drawbacks Of Systems, The Challenges Faced And Applications With Future Directions For HAR. We Also Present Some Accessible Problems And Ideas That Should Be Discussed In Future Research For The HAR System Utilizing Machine Learning And Deep Learning Principles Due To ThPeir Strong Relevance.

AIR CANVAS APPLICATION USING OPENCV AND NUMPY IN PYTHON

Writing In Air Has Been One Of The Most Fascinating And Challenging Research Areas In Field Of Image Processing And Pattern Recognition In The Recent Years. It Contributes Immensely To The Advancement Of An Automation Process And Can Improve The Interface Between Man And Machine In Numerous Applications. Several Research Works Have Been Focusing On New Techniques And Methods That Would Reduce The Processing Time While Providing Higher Recognition Accuracy. Object Tracking Is Considered As An Important Task Within The Field Of Computer Vision. The Invention Of Faster Computers, Availability Of Inexpensive And Good Quality Video Cameras And Demands Of Automated Video Analysis Has Given Popularity To Object Tracking Techniques. Generally, Video Analysis Procedure Has Three Major Steps: Firstly, Detecting Of The Object, Secondly Tracking Its Movement From Frame To Frame And Lastly Analysing The Behaviour Of That Object. For Object Tracking, Four Different Issues Are Taken Into Account; Selection Of Suitable Object Representation, Feature Selection For Tracking, Object Detection And Object Tracking. In Real World, Object Tracking Algorithms Are The Primarily Part Of Different Applications Such As: Automatic Surveillance, Video Indexing And Vehicle Navigation Etc.

BLIND PEOPLE GUIDENCE- Object Detection For Visually Impaired People Using SSD Algorithm

Visually Impaired People Are Unaware Of The Danger That They Are Facing In Their Life. They May Face Many Challenges While Performing Their Daily Activity Even In Their Familiar Environments. Vision Is The Necessary Human Senses And It Plays The Important Role In Human Perception About Surrounding Environment. Hence, There Are Variety Of Computer Vision Products And Services Which Are Used In The Development Of New Electronic Aids For Those Blind People. In This Paper We Designed To Provide Navigation To Those People. It Guides The People About The Object As Well As Provides The Distance Of The Object. The Algorithm Itself Calculates The Distance Of The Object. Here It Also Provides The Audio Jack To Insist Them About The Object. Here We Are Using SSD Algorithm For Object Detection And Calculating The Distance Of The Object By Using Monodepth Algorithm.

CROWD SOCIAL DISTANCE MEASUREMENT AND MASK DETECTION- Monitoring Pandemic Precautionary Protocols Using Real-time Surveillance And Artificial Intelligence

The Worst Possible Situation Faced By Humanity, COVID-19, Is Proliferating Across More Than 180 Countries And About 37,000,000 Confirmed Cases, Along With 1,000,000 Deaths Worldwide As Of October 2020. The Absence Of Any Medical And Strategic Expertise Is A Colossal Problem, And Lack Of Immunity Against It Increases The Risk Of Being Affected By The Virus. Since The Absence Of A Vaccine Is An Issue, Social Spacing And Face Covering Are Primary Precautionary Methods Apt In This Situation. This Study Proposes Automation With A Deep Learning Framework For Monitoring Social Distancing Using Surveillance Video Footage And Face Mask Detection In Public And Crowded Places As A Mandatory Rule Set For Pandemic Terms Using Computer Vision. The Paper Proposes A Framework Is Based On YOLO Object Detection Model To Define The Background And Human Beings With Bounding Boxes And Assigned Identifications. In The Same Framework, A Trained Module Checks For Any Unmasked Individual. The Automation Will Give Useful Data And Understanding For The Pandemic's Current Evaluation; This Data Will Help Analyse The Individuals Who Do Not Follow Health Protocol Norms.

DRIVER DROWSINESS DETECTION USING EYE GAZE DETECTION- Synchronous System For Driver Drowsiness Detection Using Convolutional Neural Network And Computer Vision

One Of The Major Reasons Behind Car Accidents Is The Drowsy Nature Acquired By A Driver While Driving Any Vehicle. Owing To The Ongoing Scenario, In This Project, We Aim To Develop A Real Time Driver Drowsiness Detection System In Order To Detect The Drivers' Fatigue Status, Such As Dozing, Flickering Of Eye Lids And Time Span Of Eye Closure Without Having To Equip Their Bodies With Devices. The Objective Of This Project Is To Build A Drowsiness Detection System That Will Detect That A Person's Eyes Are Closed For A Few Seconds. This System Will Alert The Driver When Drowsiness Is Detected. This Approach Is Based On The Convolutional Neural Network That Can Be Implemented On Android Applications With High Accuracy. Apart From CNN, Computer Vision Also Plays A Major Role To Detect The Drowsiness Pattern Of The Driver. Cloud Architecture Has Also Proved To Be Beneficial In Case Of Capturing And Analyzing Real Time Video Streams.

DRONE MOVEMENT ANALYSIS WITH REINFORCEMENT LEARNING Q- LEARNING Simulation Of Drone Controller Using Reinforcement Learning AI With Hyper Parameter Optimization

Drone Is One Of The Latest Drone Technologies That Grows With Multiple Applications; One Of The Critical Applications Is For Fire-fighting Drones Such As Water Hose Carrying For Firefighting. One Of The Main Challenges Of The Drone Technologies Is The Non-linear Dynamic Movement Caused By A Variety Of Fire Conditions. One Solution Is To Use A Nonlinear Controller Such As Reinforcement Learning. In This Paper, Reinforcement Learning Has Been Applied As Their Key Control System To Improve The Conventional Approach, Which Is The Agent (drone) That Will Interact With The Environment Without Need Of The Controller For The Flying Process. This Paper Is Introduced An Optimization Method For The Hyperparameter In Order To Achieve A Better Reward. In Addition, We Only Concentrate On The Learning Rate (alpha) And Potential Reward Factor Discount (gamma) For Optimization In This Paper. From This Optimization, The Better Performance And Response From Our Result By Using Alpha = 0.1 & Gamma = 0.8 With Reward Produced 6100 And It Takes 49 Seconds In The Learning Process.

OBJECT DETECTION USING CAFFE CNN YOLOV AND SSD- Object Detection And Tracking For Community Surveillance Using Transfer Learning

Automation Of Video Surveillance Is Gaining Extensive Interest Recently, Considering The Public Security Issues. In Recent Times, A Systematic And Exact Detection Of An Object Is A Foremost Point In Computer Vision Technology. With The Unfolding Of Recent Deep Learning Techniques, The Precision Of Detecting An Object Has Increased Greatly Thereby Igniting The Interest In This Area To Large Extent. Also, With The Advent Of Automatic Driving Electric Cars, The Accurate Detection Of Objects Has Gained Phenomenal Importance. The Main Aim Is To Integrate The State-of-the-art Deep Learning Method On Pedestrian Object Detection In Real-time With Improved Accuracy. One Of The Crucial Problems In Deep Learning Is Using Computer Vision Techniques, Which Tend To Slow Down The Process With Trivial Performance. In This Work, An Improved Yolov3 Transfer Learning-based Deep Learning Technique Is Used For Object Detection. It Is Also Shown That This Approach Can Be Used For Solving The Problem Of Object Detection In A Sustained Manner Having The Ability To Further Separate Occluded Objects. Moreover, The Use Of This Approach Has Enhanced The Accuracy Of Object Detection. The Network Used Is Trained On A Challenging Data Set And The Output Obtained Is Fast And Precise Which Is Helpful For The Application That Requires Object Detection.

SELF DRIVING CAR USING COMPUTER VISION- Simulation Of Self-driving Car Using Deep Learning

The Rapid Development Of Artificial Intelligence Has Revolutionized The Area Of Autonomous Vehicles By Incorporating Complex Models And Algorithms. Self-driving Cars Are Always One Of The Biggest Inventions In Computer Science And Robotic Intelligence. Highly Robust Algorithms That Facilitate The Functioning Of These Vehicles Will Reduce Many Problems Associated With Driving Such As The Drunken Driver Problem. In This Paper Our Aim Is To Build A Deep Learning Model That Can Drive The Car Autonomously Which Can Adapt Well To The Real-time Tracks And Does Not Require Any Manual Feature Extraction. This Research Work Proposes A Computer Vision Model That Learns From Video Data. It Involves Image Processing, Image Augmentation, Behavioural Cloning And Convolutional Neural Network Model. The Neural Network Architecture Is Used To Detect Path In A Video Segment, Linings Of Roads, Locations Of Obstacles, And Behavioural Cloning Is Used For The Model To Learn From Human Actions In The Video.

VIOLENCE DETECTION- Violence Detection From Video Under 2D Spatio-Temporal Representations

Action Recognition In Videos, Especially For Violence Detection, Is Now A Hot Topic In Computer Vision. The Interest Of This Task Is Related To The Multiplication Of Videos From Surveillance Cameras Or Live Television Content Producing Complex 2D+t Data. State-of-the-art Methods Rely On End-to-end Learning From 3D Neural Network Approaches That Should Be Trained With A Large Amount Of Data To Obtain Discriminating Features. To Face These Limitations, We Present In This Article A Method To Classify Videos For Violence Recognition Purpose, By Using A Classical 2D Convolutional Neural Network (CNN). The Strategy Of The Method Is Two-fold: (1) We Start By Building Several 2D Spatio-temporal Representations From An Input Video, (2) The New Representations Are Considered To Feed The CNN To The Train/test Process. The Classification Decision Of The Video Is Carried Out By Aggregating The Individual Decisions From Its Different 2D Spatio-temporal Representations. An Experimental Study On Public Datasets Containing Violent Videos Highlights The Interest Of The Presented Method.

RARE ITEM IDENDIFICATION USING APRIORI ALGORITHM- Modern Applications And Challenges For Rare Itemset Mining

Data Mining Is The Process Of Extracting Useful Unknown Knowledge From Large Datasets. Frequent Itemset Mining Is The Fundamental Task Of Data Mining That Aims At Discovering Interesting Itemsets That Frequently Appear Together In A Dataset. However, Mining Infrequent (rare) Itemsets May Be More Interesting In Many Real-life Applications Such As Predicting Telecommunication Equipment Failures, Genetics, Medical Diagnosis, Or Anomaly Detection. In This Paper, We Survey Up-to-date Methods Of Rare Itemset Mining. The Main Goal Of This Survey Is To Provide A Comprehensive Overview Of The State-of-the-art Algorithms Of Rare Itemset Mining And Its Applications. The Main Contributions Of This Survey Can Be Summarized As Follows. In The First Part, We Define The Task Of Rare Itemset Mining By Explaining Key Concepts And Terminology, Motivation Examples, And Comparisons With Underlying Concepts. Then, We Highlight The State-of-art Methods For Rare Itemsets Mining. Furthermore, We Present Variations Of The Task Of Rare Itemset Mining To Discuss Limitations Of Traditional Rare Itemset Mining Algorithms. After That, We Highlight The Fundamental Applications Of Rare Itemset Mining. In The Last, We Point Out Research Opportunities And Challenges For Rare Itemset Mining For Future Research.

STREAMING DATA CLASSIFICATION- Diversity In Ensemble Model For Classification Of Data Streams With Concept Drift

Data Streams Can Be Defined As The Continuous Stream Of Data In Many Forms Coming From Different Sources. Data Streams Are Usually Non-stationary With Continually Changing Their Underlying Structure. Solving Of Predictive Or Classification Tasks On Such Data Must Consider This Aspect. Traditional Machine Learning Models Applied On The Drifting Data May Become Invalid In The Case When A Concept Change Appears. To Tackle This Problem, We Must Utilize Special Adaptive Learning Models, Which Utilize Various Tools Able To Reflect The Drifting Data. One Of The Most Popular Groups Of Such Methods Are Adaptive Ensembles. This Paper Describes The Work Focused On The Design And Implementation Of A Novel Adaptive Ensemble Learning Model, Which Is Based On The Construction Of A Robust Ensemble Consisting Of A Heterogeneous Set Of Its Members. We Used K-NN, Naive Bayes And Hoeffding Trees As Base Learners And Implemented An Update Mechanism, Which Considers Dynamic Class-weighting And Q Statistics Diversity Calculation To Ensure The Diversity Of The Ensemble. The Model Was Experimentally Evaluated On The Streaming Datasets, And The Effects Of The Diversity Calculation Were Analyzed.

AIR AND WEATHER QUALITY- Machine Learning To Improve Numerical Weather Forecasting

This Paper Presents A Brief Overview Of Trends In Numerical Weather Prediction, Difficulties, And The Nature Of Their Occurrence, The Existing And Promising Ways To Overcome Them. The Neural Network Architecture Is Proposed As A Promising Approach To Increase The Accuracy Of The 2m Temperature Forecast Given By The COSMO Regional Model. This Architecture Allows Predicting Errors Of The Atmospheric Model Forecasts With Their Further Corrections. Experiments Are Conducted With Different Histories Of Regional Model Errors. The Number Of Epochs After Which Network Overfitting Happens Is Determined. It Is Shown That The Proposed Architecture Makes It Possible To Achieve An Improvement Of A 2m Temperature Forecast In Approximately 50% Of Cases.

COVID AND GDP GROWTH RATE PREDICTION- Forecasting The Impact Of COVID-19 On GDP Based On Adaboost

Covid-19 Pandemic Has A Unique Impact On The Economy As Other Infectious Diseases. Epidemics Affect People's Daily Consumption Activities, For Example, By Causing Them To Shop Less, Travel Less, Consume Less And Invest Less. The Reduction Of A Large Number Of Economic Activities Leads To The Suppression Of Social Demand And The Reduction Of Consumption Level, Which Further Affects The GDP Of Various Countries Around The World. It Is Necessary To Investigate And Analyze The Impact Of The Epidemic On GDP In Order To Control And Analyze The Economic Situation Under The Impact Of The Epidemic. In This Paper, We Take The Impact Of COVID-19 On The GDP Of Each Country As A Regression Problem, And Propose To Forecast GDP Through Feature Engineering Combined With Aaboost Model. The Model Was Tested On More Than 50,000 Data Records From More Than 200 Countries Provided By The Kaggle Platform To Prove The Validity. The Experiment Shows That Adaboost Has Stronger Robustness Compared With Other Methods, Such As Random Forest, SVR. Adaboost Improves The MSE Of Random Forest By 2.39 And SVR By 0.38.

DIABETICS PATIENTS READMISSION PREDICTION- Prediction Of Diabetic Patient Readmission Using Machine Learning

Hospital Readmissions Pose Additional Costs And Discomfort For The Patient And Their Occurrences Are Indicative Of Deficient Health Service Quality, Hence Efforts Are Generally Made By Medical Professionals In Order To Prevent Them. These Endeavors Are Especially Critical In The Case Of Chronic Conditions, Such As Diabetes. Recent Developments In Machine Learning Have Been Successful At Predicting Readmissions From The Medical History Of The Diabetic Patient. However, These Approaches Rely On A Large Number Of Clinical Variables Thereby Requiring Deep Learning Techniques. This Article Presents The Application Of Simpler Machine Learning Models Achieving Superior Prediction Performance While Making Computations More Tractable.

HOTEL BOOKING WITH DATA ANALYSIS- Performance Analysis Of Machine Learning Techniques To Predict Hotel Booking Cancellations In Hospitality Industry

Hotel Booking Cancellation Is Provided A Substantial Effects On Demand Management Decisions In The Hospitality Industry. The Goal Of This Work Is To Investigate The Effects Of Different Machine Learning Methods In Hotel Booking Cancellation Process. In This Work, We Gathered A Hotel Booking Cancellation Dataset From Kaggle Data Repository. Then, Different Feature Transformation Techniques Were Implemented Into Primary Dataset And Generate Transformed Datasets. Further, We Reduced Insignificant Variables Using Feature Selection Methods. Therefore, Various Classifiers Were Employed Into Primary And Generated Subsets. The Effects Of The Machine Learning Methods Were Evaluated And Explored The Best Approaches In This Step. Among All Of These Methods, We Found That XGBoost Is The Most Frequent Method To Analyze These Datasets. Besides, Individual Classifiers Are Generated The Highest Result For Information Gain Feature Selection Method. This Analysis Can Be Used As The Complementary Tool To Investigate Hotel Booking Cancellation Dataset More Effectively.

SMART IRRIGATION USING IDENTIFYING PLANT DISEASE AND WEATHER CONDITION- Smart Automated Irrigation System With Disease Prediction

Precision Agriculture Have Gained Wide Popularity In Recent Years For Its High-ranking Applications Such As Remote Environment Monitoring, Disease Detection, Insects And Pests Management Etc. In Addition, The Advancement In Internet Of Things (IOT) Through Which We Can Connect Real World Objects To Obtain The Information Such As Physical Phenomenon Through Sensors In The Field Of Agriculture. This Paper Reports On The Smart Automated Irrigation System With Disease Detection. The System Design Includes Soil Moisture Sensors, Temperature Sensors, Leaf Wetness Sensors Deployed In Agriculture Field, The Sensed Data From Sensors Will Be Compared With Pre-determined Threshold Values Of Various Soil And Specific Crops. The Deployed Sensors Data Are Fed To The Arduino Uno Processor Which Is Linked To The Data Centre Wirelessly Via GSM Module. The Data Received By The Data Centre Is Stored To Perform Data Analysis Using Data Mining Technique Such As Markov Model To Detect The Possible Disease For That Condition. Finally, The Analysis Results And Observed Physical Parameters Are Transmitted To Android Smart Phone And Displayed On User Interface. The User Interface In Smart Phone Allows Remote User To Control Irrigation System By Switching, On And Off, The Motor Pump By The Arduino Based On The Commands From The Android Smart Phone.

RAIN FALL PREDICTION USING ANN- Rainfall Prediction Using Machine Learning And Deep Learning Techniques

In India, Agriculture Is The Key Point For Survival. For Agriculture, Rainfall Is Most Important. These Days Rainfall Prediction Has Become A Major Problem. Prediction Of Rainfall Gives Awareness To People And Know In Advance About Rainfall To Take Certain Precautions To Protect Their Crop From Rainfall. Many Techniques Came Into Existence To Predict Rainfall. Machine Learning Algorithms Are Mostly Useful In Predicting Rainfall. Some Of The Major Machine Learning Algorithms Are ARIMA Model(Auto-Regressive Integrated Moving Average), Artificial Neural Network, Logistic Regression, Support Vector Machine And Self Organizing Map. Two Commonly Used Models Predict Seasonal Rainfall Such As Linear And Non-Linear Models. The First Models Are ARIMA Model. While Using Artificial Neural Network(ANN) Predicting Rainfall Can Be Done Using Back Propagation NN, Cascade NN Or Layer Recurrent Network. Artificial NN Is Same As Biological Neural Networks.

RESTAURANT MANAGEMENT SYSTEM- An Android Based Restaurant Automation System With Touch Screen

Online-to-offline (O2O) Commerce Connecting Service Providers And Individuals To Address Daily Human Needs Is Quickly Expanding. In Particular, On-demand Food, Whereby Food Orders Are Placed Online By Customers And Delivered By Couriers, Is Becoming Popular. This Novel Urban Food Application Requires Highly Efficient And Scalable Real-time Delivery Services. However, It Is Difficult To Recruit Enough Couriers And Route Them To Facilitate Such Food Ordering Systems. This Paper Presents An Online Crowdsourced Delivery (OCD) Approach For On-demand Food. Facilitated By Internet-of-Things And 3G/4G/5G Technologies, Public Riders Can Be Attracted To Act As Crowdsourced Workers Delivering Food By Means Of Shared Bicycles Or Electric Motorbikes. An Online Dynamic Optimization Framework Comprising Order Collection, Solution Generation, And Sequential Delivery Processes Is Presented. A Hybrid Metaheuristic Solution Process Integrating The Adaptive Large Neighborhood Search And Tabu Search Approaches Is Developed To Assign Food Delivery Tasks And Generate High-quality Delivery Routes In A Real-time Manner. The Crowdsourced Riders Are Dynamically Shared Among Different Food Providers. Simulated Small-scale And Real-world Large-scale On-demand Food Delivery Instances Are Used To Evaluate The Performance Of The Proposed Approach. The Results Indicate That The Presented Crowdsourced Food Delivery Approach Outperforms Traditional Urban Logistics. The Developed Hybrid Optimization Mechanism Is Able To Produce High-quality Crowdsourced Delivery Routes In Less Than 120 S. The Results Demonstrate That The Presented OCD Approach Can Facilitate City-scale On-demand Food Delivery.

PIZZA ORDERING SYSTEM USING TKINTER GUI- Online Crowdsourced Delivery For On-Demand Food

Online-to-offline (O2O) Commerce Connecting Service Providers And Individuals To Address Daily Human Needs Is Quickly Expanding. In Particular, On-demand Food, Whereby Food Orders Are Placed Online By Customers And Delivered By Couriers, Is Becoming Popular. This Novel Urban Food Application Requires Highly Efficient And Scalable Real-time Delivery Services. However, It Is Difficult To Recruit Enough Couriers And Route Them To Facilitate Such Food Ordering Systems. This Paper Presents An Online Crowdsourced Delivery (OCD) Approach For On-demand Food. Facilitated By Internet-of-Things And 3G/4G/5G Technologies, Public Riders Can Be Attracted To Act As Crowdsourced Workers Delivering Food By Means Of Shared Bicycles Or Electric Motorbikes. An Online Dynamic Optimization Framework Comprising Order Collection, Solution Generation, And Sequential Delivery Processes Is Presented. A Hybrid Metaheuristic Solution Process Integrating The Adaptive Large Neighborhood Search And Tabu Search Approaches Is Developed To Assign Food Delivery Tasks And Generate High-quality Delivery Routes In A Real-time Manner. The Crowdsourced Riders Are Dynamically Shared Among Different Food Providers. Simulated Small-scale And Real-world Large-scale On-demand Food Delivery Instances Are Used To Evaluate The Performance Of The Proposed Approach. The Results Indicate That The Presented Crowdsourced Food Delivery Approach Outperforms Traditional Urban Logistics. The Developed Hybrid Optimization Mechanism Is Able To Produce High-quality Crowdsourced Delivery Routes In Less Than 120 S. The Results Demonstrate That The Presented OCD Approach Can Facilitate City-scale On-demand Food Delivery.

E-HEALTH DATA- Interoperable And Discrete EHealth Data Exchange Between Hospital And Patient

In Order To Prevent Health Risks And Provide A Better Service To The Patients That Have Visited The Hospital, There Is A Need For Monitoring The Patients After Being Released And Providing The Data Submitted By The Patient EHealth Enablers To The Medical Personnel. This Article Proposes An Architecture For Providing The Secure Exchange Of Data Between The Patient Mobile Application And The Hospital Infrastructure. The Implemented Solution Is Validated On A Laboratory Testbed.

VOICE CHATTING AND VIDEO CONFERENCING WebRTC Role In Real-time Communication And Video Conferencing

Real-time Communication (RTC) Is A New Standard And Industry-wide Effort That Expand The Web Browsing Model, Allowing Access To Information In Areas Like Social Media, Chat, Video Conferencing, And Television Over The Internet, And Unified Communication. These Systems Users Can View, Record, Remark, Or Edit Video And Audio Content Flows Using Time-critical Cloud Infrastructures That Enforce The Quality Of Services. However, There Are Many Proprietary Protocols And Codecs Available That Are Not Easily Interoperable And Scalable To Implement Multipoint Videoconference Systems. WebRTC (Web Real-Time Communication) Is A State-of-the-Art Open Technology That Makes Real-time Communication Capabilities In Audio, Video, And Data Transmission Possible In Real-time Communication Through Web Browsers Using JavaScript APIs (Application Programming Interfaces) Without Plug-ins. This Paper Aims To Introduce The P2P Video Conferencing System Based On Web Real-Time Communication (WebRTC). In This Paper, We Have Proposed A Web-based Peer-to-peer Real-time Communication System Using The Mozilla Firefox Together With The ScaleDrone Service That Enables Users To Communicate With Highspeed Data Transmission Over The Communication Channel Using WebRTC Technology, HTML5 And Use Node.js Server Address. Our Experiments Show That WebRTC Is A Capable Building Block For Scalable Live Video Conferencing Within A Web Browser.

VIDEO STERNOGRAPHY- A New Video Steganography Scheme Based On Shi-Tomasi Corner Detector

Recent Developments In The Speed Of The Internet And Information Technology Have Made The Rapid Exchange Of Multimedia Information Possible. However, These Developments In Technology Lead To Violations Of Information Security And Private Information. Digital Steganography Provides The Ability To Protect Private Information That Has Become Essential In The Current Internet Age. Among All Digital Media, Digital Video Has Become Of Interest To Many Researchers Due To Its High Capacity For Hiding Sensitive Data. Numerous Video Steganography Methods Have Recently Been Proposed To Prevent Secret Data From Being Stolen. Nevertheless, These Methods Have Multiple Issues Related To Visual Imperceptibly, Robustness, And Embedding Capacity. To Tackle These Issues, This Paper Proposes A New Approach To Video Steganography Based On The Corner Point Principle And LSBs Algorithm. The Proposed Method First Uses Shi-Tomasi Algorithm To Detect Regions Of Corner Points Within The Cover Video Frames. Then, It Uses 4-LSBs Algorithm To Hide Confidential Data Inside The Identified Corner Points. Besides, Before The Embedding Process, The Proposed Method Encrypts Confidential Data Using Arnold’s Cat Map Method To Boost The Security Level.

SECRET KEY GENERATION- Securing Private Key Using New Transposition Cipher Technique

The Security Of Any Public Key Cryptosystem Depends On The Private Key Thus, It Is Important That Only An Authorized Person Can Have Access To The Private Key. The Paper Presents A New Algorithm That Protects The Private Key Using The Transposition Cipher Technique. The Performance Of The Proposed Technique Is Evaluated By Applying It In The RSA Algorithm's Generated Private Keys Using 512-bit, 1024-bit, And 2048-bit, Respectively. The Result Shows That The Technique Is Practical And Efficient In Securing Private Keys While In Storage As It Produced High Avalanche Effect.

QR CODE GENERATION AND RECOGNITION FOR DATA SECURITY- A Desktop Application Of QR Code For Data Security And Authentication

Initially The Barcodes Have Been Widely Used For The Unique Identification Of The Products. Quick Response I.e. QR Codes Are 2D Representation Of Barcodes That Can Embed Text, Audio, Video, Web URL, Phone Contacts, Credentials And Much More. This Paper Primarily Deals With The Generation Of QR Codes For Question Paper. We Have Proposed Encryption Of Question Paper Data Using AES Encryption Algorithm. The Working Of The QR Codes Is Based On Encrypting It To QR Code And Scanning To Decrypt It. Furthermore, We Have Reduced The Memory Storage By Redirecting To A Webpage Through The Transmission And Online Acceptance Of Data.

E-HEALTH DATA- Detecting The Malicious Application Using FRAppE

Communication Technology Has Completely Occupied All The Areas Of Applications. Last Decade Has However Witnessed A Drastic Evolution In Information And Communication Technology Due To The Introduction Of Social Media Network. Business Growth Is Further Achieved Via These Social Media. Nevertheless, Increase In The Usage Of Online Social Networks (OSN) Such As Face Book, Twitter, Instagram Etc Has However Led To The Increase In Privacy And Security Concerns. Third Party Applications Are One Of The Many Reasons For Facebook Attractiveness. Regrettably, The Users Are Unaware Of Detail That A Lot Of Malicious Facebook Applications Provide On Their Profile. The Popularity Of These Third Party Applications Is Such That There Are Almost 20 Million Installations Per Day. But Cyber Criminals Have Appreciated The Popularity Of Third Party Applications And The Possibility Of Using These Apps For Distributing The Malware And Spam. This Paper Proposes A Method To Categorize A Given Application As Malicious Or Safe By Using FRAppE (Facebook's Rigorous Application Evaluator), Possibly One Of The First Tool For Detecting Malicious Apps On The Facebook. To Develop The FRAppE, The Data Is Gathered From MyPagekeeper Application, A Website That Provides Significant Information About Various Third Party Applications And Their Insight Into Their Behavior.

NEGATIVE REVIEW AND POSITIVE REVIEW CLASSIFICATION- A Review On Machine Learning Based Students Academic Performance Prediction Systems

Prediction Of Academic Performance Of Students Beforehand Provides Scope To Universities To Lower Their Dropout Rate And Help The Students In Improving Their Performance. In This Field, Research Is Being Done To Find Out Which Algorithm Is Best To Use And Which Features Should Be Considered While Predicting The Academic Performance Of Students. This Kind Of Research Work Has Been Increasing Over The Years. This Paper Performs A Survey On The Techniques Used In Various Research Papers For Academic Performance Prediction And Also Point Out The Limitations If Any, In The Methodology Used.

REVIEW PREDICTION FOR AMAZON DATASET- Opinion Mining Based Fake Product Review Monitoring And Removal System

Fake Review Detection And Its Elimination From The Given Dataset Using Different Natural Language Processing (NLP) Techniques Is Important In Several Aspects. In This Article, The Fake Review Dataset Is Trained By Applying Two Different Machine Learning (ML) Models To Predict The Accuracy Of How Genuine Are The Reviews In A Given Dataset. The Rate Of Fake Reviews In E-commerce Industry And Even Other Platforms Is Increasing When Depend On Product Reviews For The Item Found Online On Different Websites And Applications. The Products Of The Company Were Trusted Before Making A Purchase. So This Fake Review Problem Must Be Addressed So That These Large E-commerce Industries Such As Flipkart, Amazon, Etc. Can Rectify This Issue So That The Fake Reviewers And Spammers Are Eliminated To Prevent Users From Losing Trust On Online Shopping Platforms. This Model Can Be Used By Websites And Applications With Few Thousands Of Users Where It Can Predict The Authenticity Of The Review Based On Which The Website Owners Can Take Necessary Action Towards Them. This Model Is Developed Using Naïve Bayes And Random Forest Methods. By Applying These Models One Can Know The Number Of Spam Reviews On A Website Or Application Instantly. To Counter Such Spammers, A Sophisticated Model Is Required In Which A Need To Be Trained On Millions Of Reviews. In This Work “amazon Yelp Dataset” Is Used To Train The Models And Its Very Small Dataset Is Used For Training On A Very Small Scale And Can Be Scaled To Get High Accuracy And Flexibility.

CHATBOT FOR EMOTIOON RECOGNITION- Model Of Multi-turn Dialogue In Emotional Chatbot

The Intent Recognition And Natural Language Understanding Of Multi-turn Dialogue Is Key For The Commercialization Of Chatbots. Chatbots Are Mainly Used For The Processing Of Specific Tasks, And Can Introduce Products To Customers Or Solve Related Problems, Thus Saving Human Resources. Text Sentiment Recognition Enables A Chatbot To Know The User's Emotional State And Select The Best Response, Which Is Important In Medical Care. In This Study, We Combined The Multi-turn Dialogue Model And Sentiment Recognition Model To Develop A Chatbot, That Is Designed For Used In Daily Conversations Rather Than For Specific Tasks. Thus, The Chatbot Has The Ability To Provide The Robot's Emotions As Feedback While Talking With A User. Moreover, It Can Exhibit Different Emotional Reactions Based On The Content Of The User's Conversation.

VIRTUAL ASSISTANT USING NATURAL LANGUAGE PROCESSING- Intelligent Personal Assistant - Implementing Voice Commands Enabling Speech Recognition

Intelligent Personal Assistant (IPA) Is A Software Agent Performing Tasks On Behalf Of An Human Or Individual I Based On Commands Or Questions Which Are Similar To Chat Bots. They Are Also Referred As Intelligent Virtual Assistant Which Interprets Human Speech And Respond Via Synthesized Voices. IPAs And IVAs Finds Their Usage In Various Applications Such As Home Automation, Manage To-do Tasks And Media Playback Through Voice. This Paper Aims To Propose Speech Recognition Systems And Dealing With Creating A Virtual Personal Assistant. The Existing System Serves On The Internet And Is Maintained By The Third Party. This Application Shall Protect Personal Data From Others And Use The Local Database, Speech Recognition And Synthesiser. A Parser Named SURR(Semantic Unification And Reference Resolution) Is Employed To Recognise The Speech. Synthesizer Uses Text To Phoneme.

CHATBOT FOR HEALTH CARE AND DIET PLAN- Artificial Intelligence AI Dietician For Personal Use

Poor Nutrition Can Lead To Reduced Immunity, Increased Susceptibility To Disease, Impaired Physical And Mental Development, And Reduced Productivity. A Conversational Agent Can Support People As A Virtual Coach, However Building Such Systems Still Have Its Associated Challenges And Limitations. This Paper Describes The Background And Motivation For Chatbot Systems In The Context Of Healthy Nutrition Recommendation. We Discuss Current Challenges Associated With Chatbotapplication, We Tackled Technical, Theoretical, Behavioural, And Social Aspects Of The Challenges. We Then Propose A Pipeline To Be Used As Guidelines By Developers To Implement Theoretically And Technically Robust Chatbot Systems.

VOICE BASED ASCENDING AND DECENDING ORDER EXECUTION- Voice Based Sorting Using Pyaudio And Also Searching Operations

Voice Control Is A Major Growing Feature That Change The Way People Can Live. The Voice Assistant Is Commonly Being Used In Smartphones And Laptops. AI-based Voice Assistants Are The Operating Systems That Can Recognize Human Voice And Respond Via Integrated Voices. This Voice Assistant Will Gather The Audio From The Microphone And Then Convert That Into Text, Later It Is Sent Through GTTS (Google Text To Speech). GTTS Engine Will Convert Text Into Audio File In English Language, Then That Audio Is Played Using Play Sound Package Of Python Programming Language.

BLOOD CANCER CELL CLASSIFICATION USING DENSENET- Medical Image Classification Using A Light-Weighted Hybrid Neural Network Based On PCANet And DenseNet

Medical Image Classification Plays An Important Role In Disease Diagnosis Since It Can Provide Important Reference Information For Doctors. The Supervised Convolutional Neural Networks (CNNs) Such As DenseNet Provide The Versatile And Effective Method For Medical Image Classification Tasks, But They Require Large Amounts Of Data With Labels And Involve Complex And Time-consuming Training Process. The Unsupervised CNNs Such As Principal Component Analysis Network (PCANet) Need No Labels For Training But Cannot Provide Desirable Classification Accuracy. To Realize The Accurate Medical Image Classification In The Case Of A Small Training Dataset, We Have Proposed A Light-weighted Hybrid Neural Network Which Consists Of A Modified PCANet Cascaded With A Simplified DenseNet. The Modified PCANet Has Two Stages, In Which The Network Produces The Effective Feature Maps At Each Stage By Convoluting Inputs With Various Learned Kernels. The Following Simplified DenseNet With A Small Number Of Weights Will Take All Feature Maps Produced By The PCANet As Inputs And Employ The Dense Shortcut Connections To Realize Accurate Medical Image Classification. To Appreciate The Performance Of The Proposed Method, Some Experiments Have Been Done On Mammography And Osteosarcoma Histology Images. Experimental Results Show That The Proposed Hybrid Neural Network Is Easy To Train And It Outperforms Such Popular CNN Models As PCANet, ResNet And DenseNet In Terms Of Classification Accuracy, Sensitivity And Specificity.

BREAST CANCER CLASSIFICATION WITH FEATURE DATA- Breast Cancer Detection-Based Feature Optimization Using Firefly Algorithm And Ensemble Classifier

The Accurate Detection Of The Malignant Cell Of Breast Cancer, Decrease The Rate Of Mortality Of Women Around The World. The Process Of Feature Optimization, Increase The Probability Of Feature Selection And Mapping Of Data. This Paper Proposed Ensemble-based Classifier For Detection Of Breast Cancer On An Early Stage. The Proposed Ensemble Classifier Support Vector Machine Is A Base Classifier, And The Other Is Boost Classifier. The Firefly Algorithm Reduces The Variance Of Breast Cancer Features For The Selection Of Feature Components For The Classification Algorithm. The Most Dominated Work Is The Extraction Of Features Of Breast Cancer Image. For The Extraction Of Features Applied Wavelet Packet Transform, Wavelet Packet Transform Overcome The Limitation Of Wavelet Transform And Increase The Diversity Of Feature Extraction Process. The Proposed Algorithm Implemented In MATLAB Software And Tested With The Very Reputed Breast Cancer Image Dataset, MIAS And DDSM. The Proposed Algorithm’s Performance Measured With Standard Parameters Such As Accuracy, Specificity, Sensitivity, And MCC. The Evaluated Results Indicate That The Proposed Algorithm Is Better Than DWPT, SVM And BMC. The Increasing Ratio Of The Classification Algorithm Is 8% Instead Of Existing Algorithms.

HUMAN DISEASE PREDICTION USING SYMPTOMS BY MACHINE LEARNING- Symptoms Based Disease Prediction Using Machine Learning Techniques

Computer Aided Diagnosis (CAD) Is Quickly Evolving, Diverse Field Of Study In Medical Analysis. Significant Efforts Have Been Made In Recent Years To Develop Computer-aided Diagnostic Applications, As Failures In Medical Diagnosing Processes Can Result In Medical Therapies That Are Severely Deceptive. Machine Learning (ML) Is Important In Computer Aided Diagnostic Test. Object Such As Body-organs Cannot Be Identified Correctly After Using An Easy Equation. Therefore, Pattern Recognition Essentially Requires Training From Instances. In The Bio Medical Area, Pattern Detection And ML Promises To Improve The Reliability Of Disease Approach And Detection. They Also Respect The Dispassion Of The Method Of Decisions Making. ML Provides A Respectable Approach To Make Superior And Automated Algorithm For The Study Of High Dimension And Multi - Modal Bio Medicals Data. The Relative Study Of Various ML Algorithm For The Detection Of Various Disease Such As Heart Disease, Diabetes Disease Is Given In This Survey Paper. It Calls Focus On The Collection Of Algorithms And Techniques For ML Used For Disease Detection And Decision Making Processes.

COVID FOR PNEUMONIA CLASSIFICATION USING DENSENET- Deep Learning Based Detection And Segmentation Of COVID-19 - Pneumonia On Chest X-ray Image

The Outbreaks Of COVID-19 Virus Have Crossed The Limit To Our Expectation And It Breaks All Previous Records Of Virus Outbreaks. The Effect Of Corona Virus Causes A Serious Illness May Result In Death As A Consequence Of Substantial Alveolar Damage And Progressive Respiratory Failure. Automatic Detection And Classification Of This Virus From Chest X-ray Image Using Computer Vision Technology Can Be Very Useful Complement With Respect To The Less Sensitive Traditional Process Of Detecting COVID-19 I.e. Reverse Transcription Polymerase Chain Reaction (RT-PCR). This Automated Process Offers A Great Potential To Enhance The Conventional Healthcare Tactic For Tackling COVID-19 And Can Mitigate The Shortage Of Trained Physicians In Remote Communities. Again, The Segmentation Of The Infected Regions From Chest X-ray Image Can Help The Medical Specialists To View Insights Of The Affected Region. So, In This Paper We Have Used Deep Learning Based Ensemble Model For The Classification Of COVID-19, Pneumonia And Normal X-ray Image And For Segmentation We Have Used DenseNet Based U-Net Architecture To Segment The Affected Region. For Making The Ground Truth Mask Image Which Is Needed For Segmenting Purpose, We Have Used Amazon SageMaker Ground Truth Tool To Manually Crop The Activation Region (discriminative Image Regions By Which CNN Identify A Specific Class Using Grad-CAM Algorithm) Of The X-ray Image. We Have Found The Classification Accuracy 99.2% On The Available X-ray Dataset And 92% Average Accuracy From The Segmentation Process.

FALL DETECTION USING MOTION SENSOR DATA- Human Falling Detection Algorithm Based On Multisensor Data Fusion With SVM

Falling Is A Common Phenomenon In The Life Of The Elderly, And It Is Also One Of The 10 Main Causes Of Serious Health Injuries And Death Of The Elderly. In Order To Prevent Falling Of The Elderly, A Real-time Fall Prediction System Is Installed On The Wearable Intelligent Device, Which Can Timely Trigger The Alarm And Reduce The Accidental Injury Caused By Falls. At Present, Most Algorithms Based On Single-sensor Data Cannot Accurately Describe The Fall State, While The Fall Detection Algorithm Based On Multisensor Data Integration Can Improve The Sensitivity And Specificity Of Prediction. In This Study, We Design A Fall Detection System Based On Multisensor Data Fusion And Analyze The Four Stages Of Falls Using The Data Of 100 Volunteers Simulating Falls And Daily Activities. In This Paper, Data Fusion Method Is Used To Extract Three Characteristic Parameters Representing Human Body Acceleration And Posture Change, And The Effectiveness Of The Multisensor Data Fusion Algorithm Is Verified. The Sensitivity Is 96.67%, And The Specificity Is 97%. It Is Found That The Recognition Rate Is The Highest When The Training Set Contains The Largest Number Of Samples In The Training Set. Therefore, After Training The Model Based On A Large Amount Of Effective Data, Its Recognition Ability Can Be Improved, And The Prevention Of Fall Possibility Will Gradually Increase. In Order To Compare The Applicability Of Random Forest And Support Vector Machine (SVM) In The Development Of Wearable Intelligent Devices, Two Fall Posture Recognition Models Were Established, Respectively, And The Training Time And Recognition Time Of The Models Are Compared. The Results Show That SVM Is More Suitable For The Development Of Wearable Intelligent Devices.

BREAST CANCER CLASSIFICATION- Breast Cancer Detection And Classification

Blood Cancer (Leukemia) Is One Of The Leading Causes Of Death Among Humans. The Pace Of Healing Depends Mainly On Early Detection And Diagnosis Of A Disease. The Main Reason Behind Occurrence Of Leukemia Is When Bone Marrow Produces A Lot Of Abnormal White Blood Cells This Happens. Microscopic Study On Images Is Done By Hematologists Who Make Use Of Human Blood Samples, From Which It Leads To The Requirement Of Following Methods, Which Are Microscopic Color Imaging, Image Segmentation, Clustering And Classification Which Allows Easy Identification Of Patients Suffering From This Disease. Microscopic Imaging Allows For Various Methods Of Detecting Blood Cancer In Visible And Immature White Blood Cells. Identifying Leukemia Early And Quickly Greatly Helps Practitioners In Providing Appropriate Treatment To Patients. Initially To Start With, Segmentation Stage Is Achieved By Segregating White Blood Cells From Other Blood Components I.e. Erythrocytes And Platelets By Using Statistical Parameters Such As Mean, Standard Deviation. For Diagnosing Prediction Of Leukemia, Geometrical Features Such As Area, Perimeter Of The White Blood Cell Nucleuses Investigated. In The Proposed Methodology We Make Use Of K-means, For Identifying Cancerous Stages And Its Early Detection. Experimentation And Results Were Found To Be Promising With The Accuracy Of 90% Identification Of The Cancer Cells.

LEUKIMEA CLASSIFICATION- Detection Of Blood Cancer-Leukemia Using K-means Algorithm

Blood Cancer (Leukemia) Is One Of The Leading Causes Of Death Among Humans. The Pace Of Healing Depends Mainly On Early Detection And Diagnosis Of A Disease. The Main Reason Behind Occurrence Of Leukemia Is When Bone Marrow Produces A Lot Of Abnormal White Blood Cells This Happens. Microscopic Study On Images Is Done By Hematologists Who Make Use Of Human Blood Samples, From Which It Leads To The Requirement Of Following Methods, Which Are Microscopic Color Imaging, Image Segmentation, Clustering And Classification Which Allows Easy Identification Of Patients Suffering From This Disease. Microscopic Imaging Allows For Various Methods Of Detecting Blood Cancer In Visible And Immature White Blood Cells. Identifying Leukemia Early And Quickly Greatly Helps Practitioners In Providing Appropriate Treatment To Patients. Initially To Start With, Segmentation Stage Is Achieved By Segregating White Blood Cells From Other Blood Components I.e. Erythrocytes And Platelets By Using Statistical Parameters Such As Mean, Standard Deviation. For Diagnosing Prediction Of Leukemia, Geometrical Features Such As Area, Perimeter Of The White Blood Cell Nucleuses Investigated. In The Proposed Methodology We Make Use Of K-means, For Identifying Cancerous Stages And Its Early Detection. Experimentation And Results Were Found To Be Promising With The Accuracy Of 90% Identification Of The Cancer Cells.

ORAL CANCER CLASSIFIIACTION USING VGG16- Automated Detection And Classification Of Oral Lesions Using Deep Learning For Early Detection Of Oral Cancer

Oral Cancer Is A Major Global Health Issue Accounting For 177,384 Deaths In 2018 And It Is Most Prevalent In Low- And Middle-income Countries. Enabling Automation In The Identification Of Potentially Malignant And Malignant Lesions In The Oral Cavity Would Potentially Lead To Low-cost And Early Diagnosis Of The Disease. Building A Large Library Of Well-annotated Oral Lesions Is Key. As Part Of The MeMoSA ® (Mobile Mouth Screening Anywhere) Project, Images Are Currently In The Process Of Being Gathered From Clinical Experts From Across The World, Who Have Been Provided With An Annotation Tool To Produce Rich Labels. A Novel Strategy To Combine Bounding Box Annotations From Multiple Clinicians Is Provided In This Paper. Further To This, Deep Neural Networks Were Used To Build Automated Systems, In Which Complex Patterns Were Derived For Tackling This Difficult Task. Using The Initial Data Gathered In This Study, Two Deep Learning Based Computer Vision Approaches Were Assessed For The Automated Detection And Classification Of Oral Lesions For The Early Detection Of Oral Cancer, These Were Image Classification With ResNet-101 And Object Detection With The Faster R-CNN. Image Classification Achieved An F1 Score Of 87.07% For Identification Of Images That Contained Lesions And 78.30% For The Identification Of Images That Required Referral. Object Detection Achieved An F1 Score Of 41.18% For The Detection Of Lesions That Required Referral. Further Performances Are Reported With Respect To Classifying According To The Type Of Referral Decision. Our Initial Results Demonstrate Deep Learning Has The Potential To Tackle This Challenging Task.

EFFECTIVE HEART DISEASE PREDICTION USING HYBRID MACHINE LEARNING TECHNIQUES

Heart Disease Is One Of The Most Significant Causes Of Mortality In The World Today. Prediction Of Cardio Vascular Disease Is A Critical Challenge In The Area Of Clinical Data Analysis. Machine Learning (ML) Has Been Shown To Be Effective In Assisting In Making Decisions And Predictions From The Large Quantity Of Data Produced By The Health Care Industry. We Have Also Seen ML Techniques Being Used In Recent Developments In Different Areas Of The Internet Of Things (IoT). Various Studies Give Only A Glimpse Into Predicting Heart Disease With ML Techniques. In This Paper, We Propose A Novel Method That Aims At finding Significant Features By Applying Machine Learning Techniques Resulting In Improving The Accuracy In The Prediction Of Cardiovascular Disease. The Prediction Model Is Introduced With Different Combinations Of Features And Several Known Classification Techniques.

HUMAN SIGNATURE CLASSIFICATION- Handwritten Signature Recognition- A Convolutional Neural Network Approach

Handwritten Signature Recognition Is An Important Behavioral Biometric Which Is Used For Numerous Identification And Authentication Applications. There Are Two Fundamental Methods Of Signature Recognition, On-line Or Off-line. On-line Recognition Is A Dynamic Form, Which Uses Parameters Like Writing Pace, Change In Stylus Direction And Number Of Pen Ups And Pen Downs During The Writing Of The Signature. Off-line Signature Recognition Is A Static Form Where A Signature Is Handled As An Image And The Author Of The Signature Is Predicted Based On The Features Of The Signature. The Current Method Of Off-line Signature Recognition Predominantly Employs Template Matching, Where A Test Image Is Compared With Multiple Specimen Images To Speculate The Author Of The Signature. This Takes Up A Lot Of Memory And Has A Higher Time Complexity. This Paper Proposes A Method Of Off-line Signature Recognition Using Convolution Neural Network. The Purpose Of This Paper Is To Obtain High Accuracy Multi-class Classification With A Few Training Signature Samples. Images Are Preprocessed To Isolate The Signature Pixels From The Background/noise Pixels Using A Series Of Image Processing Techniques. Initially, The System Is Trained With 27 Genuine Signatures Of 10 Different Authors Each. A Convolution Neural Network Is Used To Predict A Test Signature Belongs To Which Of The 10 Given Authors. Different Public Datasets Are Used To Demonstrate Effectiveness Of The Proposed Solution.

ONLINE VOTING USING OTPBY DJANGO- Smart Online Voting System

Our Country, India Is The Largest Democratic Country In The World. So It Is Essential To Make Sure That The Governing Body Is Elected Through A Fair Election. India Has Only Offline Voting System Which Is Not Effective And Upto The Mark As It Requires Large Man Force And It Also Requires More Time To Process And Publish The Results. Therefore, To Be Made Effective, The System Needs A Change, Which Overcomes These Disadvantages. The New Method Does Not Force The Person's Physical Appearance To Vote, Which Makes The Things Easier. This Paper Focusses On A System Where The User Can Vote Remotely From Anywhere Using His/her Computer Or Mobile Phone And Doesn't Require The Voter To Got To The Polling Station Through Two Step Authentication Of Face Recognition And OTP System. This Project Also Allows The User To Vote Offline As Well If He/she Feels That Is Comfortable. The Face Scanning System Is Used To Record The Voters Face Prior To The Election And Is Useful At The Time Of Voting. The Offline Voting System Is Improvised With The Help Of RFID Tags Instead Of Voter Id. This System Also Enables The User The Citizens To See The Results Anytime Which Can Avoid Situations That Pave Way To Vote Tampering.

FACE RECOGNITION BASED ATTENDENCE SYSTEM- Automated Smart Attendance System Using Face Recognition

In The Human Body, The Face Is The Most Crucial Factor In Identifying Each Person As It Contains Many Vital Details. There Are Different Prevailing Methods To Capture Person's Presence Like Biometrics To Take Attendance Which Is A Time-consuming Process. This Paper Develops A Model To Classify Each Character's Face From A Captured Image Using A Collection Of Rules I.e., LBP Algorithm To Record The Student Attendance. LBP (Local Binary Pattern) Is One Among The Methods And Is Popular As Well As Effective Technique Used For The Image Representation And Classification And It Was Chosen For Its Robustness To Pose And Illumination Shifts. The Proposed ASAS (Automated Smart Attendance System) Will Capture The Image And Will Be Compared To The Image Stored In The Database. The Database Is Updated Upon The Enrolment Of The Student Using An Automation Process That Also Includes Name And Rolls Number. ASAS Marks Individual Attendance, If The Captured Image Matches The Image In The Database I.e., If Both Images Are Identical. The Proposed Algorithm Reduces Effort And Captures Day-to-day Actions Of Managing Each Student And Also Makes It Simple To Mark The Presence.

MENTAL HEALTH IDENTIFICATION USING FACE EMOTION RECOGNITION- Facial Expression Recognition And Recommendations Using Deep Neural Network With Transfer Learning

Human Facial Emotion Recognition (FER) Has Attracted The Attention Of The Research Community For Its Promising Applications. Mapping Different Facial Expressions To The Respective Emotional States Are The Main Task In FER. The Classical FER Consists Of Two Major Steps: Feature Extraction And Emotion Recognition. Currently, The Deep Neural Networks, Especially The Convolutional Neural Network (CNN), Is Widely Used In FER By Virtue Of Its Inherent Feature Extraction Mechanism From Images. Several Works Have Been Reported On CNN With Only A Few Layers To Resolve FER Problems. However, Standard Shallow CNNs With Straightforward Learning Schemes Have Limited Feature Extraction Capability To Capture Emotion Information From High-resolution Images. A Notable Drawback Of The Most Existing Methods Is That They Consider Only The Frontal Images (i.e., Ignore Profile Views For Convenience), Although The Profile Views Taken From Different Angles Are Important For A Practical FER System. For Developing A Highly Accurate FER System, This Study Proposes A Very Deep CNN (DCNN) Modeling Through Transfer Learning (TL) Technique Where A Pre-trained DCNN Model Is Adopted By Replacing Its Dense Upper Layer(s) Compatible With FER, And The Model Is Fine-tuned With Facial Emotion Data. A Novel Pipeline Strategy Is Introduced, Where The Training Of The Dense Layer(s) Is Followed By Tuning Each Of The Pre-trained DCNN Blocks Successively That Has Led To Gradual Improvement Of The Accuracy Of FER To A Higher Level. The Proposed FER System Is Verified On Eight Different Pre-trained DCNN Models (VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3 And DenseNet-161) And Well-known KDEF And JAFFE Facial Image Datasets. FER Is Very Challenging Even For Frontal Views Alone. FER On The KDEF Dataset Poses Further Challenges Due To The Diversity Of Images With Different Profile Views Together With Frontal Views. The Proposed Method Achieved Remarkable Accuracy On Both Datasets With Pre-trained Models. On A 10-fold Cross-validation Way, The Best Achieved FER Accuracies With DenseNet-161 On Test Sets Of KDEF And JAFFE Are 96.51% And 99.52%, Respectively. The Evaluation Results Reveal The Superiority Of The Proposed FER System Over The Existing Ones Regarding Emotion Detection Accuracy. Moreover, The Achieved Performance On The KDEF Dataset With Profile Views Is Promising As It Clearly Demonstrates The Required Proficiency For Real-life Applications.

NON-CONTACT HEART RATE MONITORING USING MACHINE LEARNING

Health Monitoring Is An Important Parameter To Determine The Health Status Of A Person. Measuring The Heart Rate Is An Easy Way To Gauge Our Health. Normal Heart Rate May Vary From Person To Person And A Usually High Or Low Resting Heart Rate Can Be A Sign Of Trouble. There Are Several Methods For The Measurement Of Heart Rate Monitoring Such As Ecg, Ppg Etc. Such Methods Having A Disadvantage That These Are Invasive And Have A Continuous Contact With The Human Body. In Order To Overcome This Problem A New System Is Proposed Using Camera. In This Method A Blind Source Separation Algorithm Is Used For Extracting The Heart Rate Signal From The Face Image. Viola Jones Based Face Detection Algorithm Is Used To Track The Face. FastICA Algorithm Is Exploited To Separate Heart Rate Signal From Noise And Artefacts. Machine Learning Algorithm Is Implemented To Standardize The Signal. The Data Is Successfully Tested With Real Time Video.

SMART DOOR USING WEBCAM AND FINGERPRINT- Image Processing Technique For Smart Home Security Based On The Principal Component Analysis PCA Methods

Smart Home Is One Application Of The Pervasive Computing Branch Of Science. Three Categories Of Smart Homes, Namely Comfort, Healthcare, And Security. The Security System Is A Part Of Smart Home Technology That Is Very Important Because The Intensity Of Crime Is Increasing, Especially In Residential Areas. The System Will Detect The Face By The Webcam Camera If The User Enters The Correct Password. Face Recognition Will Be Processed By The Raspberry Pi 3 Microcontroller With The Principal Component Analysis Method Using OpenCV And Python Software Which Has Outputs, Namely Actuators In The Form Of A Solenoid Lock Door And Buzzer. The Test Results Show That The Webcam Can Perform Face Detection When The Password Input Is Successful, Then The Buzzer Actuator Can Turn On When The Database Does Not Match The Data Taken By The Webcam Or The Test Data And The Solenoid Door Lock Actuator Can Run If The Database Matches The Test Data Taken By The Sensor. Webcam. The Mean Response Time Of Face Detection Is 1.35 Seconds.

SIGN LANGUAGE RECOGNITION- Real-Time Recognition Of Indian Sign Language

The Real-time Sign Language Recognition System Is Developed For Recognising The Gestures Of Indian Sign Language (ISL). Generally, Sign Languages Consist Of Hand Gestures And Facial Expressions. For Recognising The Signs, The Regions Of Interest (ROI) Are Identified And Tracked Using The Skin Segmentation Feature Of OpenCV. The Training And Prediction Of Hand Gestures Are Performed By Applying Fuzzy C-means Clustering Machine Learning Algorithm. The Gesture Recognition Has Many Applications Such As Gesture Controlled Robots And Automated Homes, Game Control, Human-Computer Interaction (HCI) And Sign Language Interpretation. The Proposed System Is Used To Recognize The Real-time Signs. Hence It Is Very Much Useful For Hearing And Speech Impaired People To Communicate With Normal People.

FACE RECOGNITION- Face Detection And Recognition System Using Digital Image Processing

While Recognizing Any Individual, The Most Important Attribute Is Face. It Serves As An Individual Identity Of Everyone And Therefore Face Recognition Helps In Authenticating Any Person's Identity Using His Personal Characteristics. The Whole Procedure For Authenticating Any Face Data Is Sub-divided Into Two Phases, In The First Phase, The Face Detection Is Done Quickly Except For Those Cases In Which The Object Is Placed Quite Far, Followed By This The Second Phase Is Initiated In Which The Face Is Recognized As An Individual. Then The Whole Process Is Repeated Thereby Helping In Developing A Face Recognition Model Which Is Considered To Be One Of The Most Extremely Deliberated Biometric Technology. Basically, There Are Two Type Of Techniques That Are Currently Being Followed In Face Recognition Pattern That Is, The Eigenface Method And The Fisherface Method. The Eigenface Method Basically Make Use Of The PCA (Principal Component Analysis) To Minimize The Face Dimensional Space Of The Facial Features. The Area Of Concern Of This Paper Is Using The Digital Image Processing To Develop A Face Recognition System.

FACE EXPRESSION RECOGNITION SYSTEM- Facial Expression Recognition With Convolutional Neural Networks

Emotions Are A Powerful Tool In Communication And One Way That Humans Show Their Emotions Is Through Their Facial Expressions. One Of The Challenging And Powerful Tasks In Social Communications Is Facial Expression Recognition, As In Non-verbal Communication, Facial Expressions Are Key. In The Field Of Artificial Intelligence, Facial Expression Recognition (FER) Is An Active Research Area, With Several Recent Studies Using Convolutional Neural Networks (CNNs). In This Paper, We Demonstrate The Classification Of FER Based On Static Images, Using CNNs, Without Requiring Any Pre-processing Or Feature Extraction Tasks. The Paper Also Illustrates Techniques To Improve Future Accuracy In This Area By Using Pre-processing, Which Includes Face Detection And Illumination Correction. Feature Extraction Is Used To Extract The Most Prominent Parts Of The Face, Including The Jaw, Mouth, Eyes, Nose, And Eyebrows. Furthermore, We Also Discuss The Literature Review And Present Our CNN Architecture, And The Challenges Of Using Max-pooling And Dropout, Which Eventually Aided In Better Performance. We Obtained A Test Accuracy Of 61.7% On FER2013 In A Seven-classes Classification Task Compared To 75.2% In State-of-the-art Classification.

Deep Learning And Audio Based Emotion Recognition

While Tightening And Expansion Of Our Facial Muscles Cause Some Changes Called Facial Expressions As A Reaction To The Different Kinds Of Emotional Situations Of Our Brain, Similarly There Are Some Physiological Changes Like Tone, Loudness, Rhythm And Intonation In Our Voice, Too. These Visual And Auditory Changes Have A Great Importance For Human-human Interaction Human-machine Interaction And Human-computer Interaction As They Include Critical Information About Humans' Emotional Situations. Automatic Emotion Recognition Systems Are Defined As Systems That Can Analyze Individual's Emotional Situation By Using This Distinctive Information. In This Study, An Automatic Emotion Recognition System In Which Auditory Information Is Analyzed And Classified In Order To Recognize Human Emotions Is Proposed. In The Study Spectral Features And MFCC Coefficients Which Are Commonly Used For Feature Extraction From Voice Signals Are Firstly Used, And Then Deep Learning-based LSTM Algorithm Is Used For Classification. Suggested Algorithm Is Evaluated By Using Three Different Audio Data Sets (SAVEE, RAVADES And RML).

AN END-TO-END OPTICAL CHARACTER RECOGNITION PIPELINE FOR INDONESIAN IDENTITY CARD

Exponential Growth Of Fake ID Cards Generation Leads To Increased Tendency Of Forgery With Severe Security And Privacy Threats. University ID Cards Are Used To Authenticate Actual Employees And Students Of The University. Manual Examination Of ID Cards Is A Laborious Activity, Therefore, In This Paper, We Propose An Effective Automated Method For Employee/student Authentication Based On Analyzing The Cards. Additionally, Our Method Also Identifies The Department Of Concerned Employee/student. For This Purpose, We Employ Different Image Enhancement And Morphological Operators To Improve The Appearance Of Input Image Better Suitable For Recognition. More Specifically, We Employ Median Filtering To Remove Noise From The Given Input Image.

STONE INSCRIPTION IDENTIFICATION USING OPTICAL CHARACTER RECOGNITION- A Survey On Ancient Marathi Script Recognition From Stone Inscriptions

Rigorous Research Has Been Done On Ancient Indian Script Character Recognition. Many Research Articles Are Published In Last Few Decades. Number Of OCR Techniques Is Available In Market, But OCR Techniques Are Not Useful For Ancient Script Recognition. But More Research Work Is Required To Recognize Ancient Marathi Scripts. This Paper Presents Different Techniques Which Are Published By Different Researchers To Recognize Ancient Scripts. Also Challenges In Recognition Of Ancient Marathi Scripts Are Discussed In This Paper.

LIE OR TRUTH DETECTION USING VGG-16

Deep Neural Networks Achieve Best Classification Accuracy On Videos. However, Traditional Methods Or Shallow Architectures Remain Competitive And Combinations Of Different Network Types Are The Usual Chosen Approach. A Reason For This Less Important Impact Of Deep Methods For Video Recognition Is The Motion Representation. The Time Has A Stronger Redundancy, And An Important Elasticity Compared To The Spatial Dimensions. The Temporal Redundancy Is Evident, But The Elasticity Within An Action Class Is Well Less Considered .Several Instances Of The Action Still Widely Differ By Their Style And Speed Of Execution.

ROAD ACCIDENT DETECTION USING VIDEO CLASSIFICATION- A Novel Approach For Road Accident Detection Using DETR Algorithm

Road Accidents Are Man-made Cataclysmic Phenomena And Are Not Generally Predictable. With Increasing Numbers Of Deaths Due To Accidents In The Roadways, A Smart And Fast Detection System For Road Accidents Is The Need Of The Hour. Often, Precious Few Seconds After The Accidents Make The Difference Between Life And Death. To Address This Problem More Efficiently, “A Novel Approach For Road Accident Detection In CCTV Videos Using DETR Algorithm” Has Been Developed To Aid In Notifying Hospitals And The Local Police At Places Where Instant Notification Is Seldom Feasible. This Paper Presents A Novel And Efficient Method For Detecting Road Accidents With DETR (Detection Transformers) And Random Forest Classifier. Objects Such As Cars, Bikes, People, Etc. In The CCTV Footage Are Detected Using The DETR And The Features Are Fed To A Random Forest Classifier For Frame Wise Classification. Each Frame Of The Video Is Classified As An Accident Frame Or A Non-accident Frame. A Total Count Of Predicted Accident Frames From Any 60 Continuous Frames Of The Video Are Considered Using A Sliding Window Technique Before The Final Decision Is Made. Simulation Results Show That The Proposed System Achieves 78.2% Detection Rate In CCTV Videos.

SPORT ACTION CLASSIFICATION USING GAN- Improving Human Pose Estimation With Self-Attention Generative Adversarial Networks

Human Pose Estimation In Images Is Challenging And Important For Many Computer Vision Applications. Large Improvements In Human Pose Estimation Have Been Achieved With The Development Of Convolutional Neural Networks. Even Though, When Encountered Some Difficult Cases Even The State-of-the-art Models May Fail To Predict All The Body Joints Correctly. Some Recent Works Try To Refine The Pose Estimator. GAN (Generative Adversarial Networks) Has Been Proved To Be Efficient To Improve Human Pose Estimation. However, GAN Can Only Learn Local Body Joints Structural Constrains. In This Paper, We Propose To Apply Self-Attention GAN To Further Improve The Performance Of Human Pose Estimation. With Attention Mechanism In The Framework Of GAN, We Can Learn Long-range Body Joints Dependencies, Therefore Enforce The Entire Body Joints Structural Constrains To Make All The Body Joints To Be Consistent. Our Method Outperforms Other State-of-the-art Methods On Two Standard Benchmark Datasets MPII And LSP For Human Pose Estimation.

SPEECH EMOTION RECOGNITION- Speech Emotion Recognition With Multiscale Area Attention And Data Augmentation

In Speech Emotion Recognition (SER), Emotional Characteristics Often Appear In Diverse Forms Of Energy Patterns In Spectrograms. Typical Attention Neural Network Classifiers Of SER Are Usually Optimized On A Fixed Attention Granularity. In This Paper, We Apply Multiscale Area Attention In A Deep Convolutional Neural Network To Attend Emotional Characteristics With Varied Granularities And Therefore The Classifier Can Benefit From An Ensemble Of Attentions With Different Scales. To Deal With Data Sparsity, We Conduct Data Augmentation With Vocal Tract Length Perturbation (VTLP) To Improve The Generalization Capability Of The Classifier. Experiments Are Carried Out On The Interactive Emotional Dyadic Motion Capture (IEMOCAP) Dataset. We Achieved 79.34% Weighted Accuracy (WA) And 77.54% Unweighted Accuracy (UA), Which, To The Best Of Our Knowledge, Is The State Of The Art On This Dataset.

AUDIO OF SPEAKER RECOGNITION- Automatic Speaker Recognition System Based On Machine Learning Algorithms

Speaker Recognition Is A Technique Used To Automatically Recognize A Speaker From A Recording Of Their Voice Or Speech Utterance. Speaker Recognition Technology Has Improved Over Recent Years And Has Become Inexpensive And And Reliable Method For Person Identification And Verification. Research In The Field Of Speaker Recognition Has Now Spanned Over Five Decades And Has Shown Fruitful Results, However There Is Not Much Work Done With Regards To South African Indigenous Languages. This Paper Presents The Development Of An Automatic Speaker Recognition System That Incorporates Classification And Recognition Of Sepedi Home Language Speakers. Four Classifier Models, Namely, Support Vector Machines, K-Nearest Neighbors, Multilayer Perceptrons (MLP) And Random Forest (RF), Are Trained Using WEKA Data Mining Tool. Auto-WEKA Is Applied To Determine The Best Classifier Model Together With Its Best Hyper-parameters. The Performance Of Each Model Is Evaluated In WEKA Using 10-fold Cross Validation. MLP And RF Yielded Good Accuracy Surpassing The State-of-the-art With An Accuracy Of 97% And 99.9% Respectively, The RF Model Is Then Implemented On A Graphical User Interface For Development Testing.

MUSIC GENRE CLASSIFICATION USING DEEP LEARNING- Music Genre Classification Classification Using Deep Learning And Neural Network Algorithms

Categorizing Music Files According To Their Genre Is A Challenging Task In The Area Of Music Information Retrieval (MIR). In This Study, We Compare The Performance Of Two Classes Of Models. The First Is A Deep Learning Approach Wherein A CNN Model Is Trained End-to-end, To Predict The Genre Label Of An Audio Signal, Solely Using Its Spectrogram. The Second Approach Utilizes Hand-crafted Features, Both From The Time Domain And The Frequency Domain. We Train Four Traditional Machine Learning Classifiers With These Features And Compare Their Performance. The Features That Contribute The Most Towards This Multi-class Classification Task Are Identified. The Experiments Are Conducted On The Audio Set Data Set And We Report An AUC Value Of 0.894 For An Ensemble Classifier Which Combines The Two Proposed Approaches.

SPEECH TO TEXT CONVERTION USING PYTHON- Design Of Voice To Text Conversion And Management Program Based On Google Cloud Speech API

Sexual Crime, Including Sexual Harassment And Sex Assault, Is Prevalent. In Particular, The Number Of Reported Cases Of Sexual Crimes Occurring In The Workplace Is Steadily Increasing. Victims Of Sexual Crime Are Required To Prove The Fact Of The Damage, But It Is Not Easy To Prove The Evidence, So The Sex Offenders Are Often Not Punished Properly Because Of Insufficient Evidence. In This Paper, We Design A Recording Service Called CCVoice. It Uses Mobile Devices To Record Everyday Life. At The Same Time, It Converts The Recorded File To Text Using Google Cloud Speech API And Save The Text File. Therefore, It Is Possible To Easily Obtain Voice Evidence When A User Is Suddenly Sexually Abused Such As Sexual Harassment Or Sex Assault.

BIG DATA ANALYSIS BY US ACCIDENT DATASET- Data Analytics Factors Of Traffic Accidents In The UK

  • Domain: PYSPARK
  • Category: PYTHON
  • Year: 2022
  • Project Code: PYPYS2101

The Traffic And Accident Datasets For This Research Are Sourced By Data.gov.uk. The Data Analytics In This Paper Comprises Three Levels Namely: Descriptive Statistical Analysis; Inferential Statistical Analysis; Machine Learning. The Aim Of The Data Analytics Is To Explore The Factors That Could Have Impact On The Number Of Accidents And Their Associated Fatalities. Some Of The Factors Investigated On Are: Time Of The Day, Day Of The Week, Month Of The Year, Speed Limits, Etc... Machine Learning Approaches Have Also Been Employed To Predict The Types Of Accident Severity.

A PRACTICAL INTERACTIVE CHESS BOARD WITH AUTOMATIC MOVEMENT CONTROL

The Interactive Chess Board Game Is Unlike Games In Its Ordinary Way. This Board Game Together With Tangible Movements Of All Pieces Is Considered To Be Users Attraction. Therefore, The New Chessboard With An Automatic Moving Mechanism For Every Piece Is Chosen. Initially, We Have Designed And Developed An Aluminum Core Structure For Positioning X And Y-axis. Furthermore, A Controllable Magnet Is Deliberated For Holding And Moving An Individual Chess Piece According To Player Manipulations. Purpose Of This Interactive Chess Board Is Applying Technology To Board Game For Excitement, Interest, Amazement, And Attraction. Arduino Microcontroller Is Used For Controlling Every Step Of Piece Movement. The Microcontroller Receives Control Information Through The User Interface And Then Moves The Chess Piece To The Destination On The Board. The Position Calculation Is Brought To Identify The Chess Piece And Drive Accurately The Stepper Motors In X And Y-axis.

SNAKE GAME- Solving The Classic Snake Game Using AI

N This Paper We Did Survey Of Various Papers Based On The Classic Snake Game And Compared Their Various Traits And Features. In This Paper We Introduce An AI Bot To Enhance The Skills Of The Player And The AI Bot Uses The Algorithms Further Discussed In This Paper. Player Can Follow The Simultaneously Running AI Bot To Play The Game Effectively. In This We Use The Classic Snake Game, For That We Present Different Algorithms Or Methods For AI Bot. It Includes Three Searching Algorithms Related To Artificial Intelligence, Best First Search, A* Search And Improved A* Search With Forward Checking, And Two Baseline Methods Random Move And Almighty Move

Property Selling Website Using Django Framework

In A Generation Led By Millennials, Technologies Are Becoming Redundant Each Year. The Organizations Are Competing On A Global Scale And Newer And Innovative Strategies Are Introduced In The Field Of Marketing To Reach Out To The Potential Buyers. Real Estate, Being One Of The Biggest Business Sectors, Needs More Efficiently Targeted Marketing Campaigns As This Is A Very Niche And Unexplored Field In The Indian Scenario. Real Estate Projects Are Highly Priced Products Which Cannot Be Sold Efficiently Without A Well Strategized Marketing Campaign So As To Reach Out To The Exact Targeted Market. The Unsold Inventories In Various Metro Cities Range From 15-60%. The Stipulated Real Estate Sector Growth Trends By Government Do Not Go Hand In Hand With The On-ground Realities Of The Piled-up Inventories. The Marketing Strategies Have Not Evolved With The Digitization Boom, And Still The Real Estate Marketing Techniques Are Conventional And Financially Heavy. Through This Study, Efforts Have Been Made To Pin Point The Existing Supply-demand Problems In The Cities Of Ahmedabad And Mumbai In The Affordable And HIG Housing Sector Specifically. Also, Suitable Solutions For Marketing Campaigns Have Been Proposed Considering The Current Market Realities For Both Cities.

EYE BLINK DETECTION- Eye Blink Detection Using Opencv Computer Vision

The Basic Nonverbal Interaction That Is Now Evolving In The Upcoming Generation Is Eye Gaze. This Eye Blink System Builds A Bridge For Communication Of People Affected With Disabilities. The Operation Is So Simple That With The Eyes Blinking At The Control Keys That Are Built In The Screen . This Type Of System Can Synthesize Speech, Control His Environment, And Give A Major Development Of Confidence In The Individual . Our Paper Mainly Enforces The Virtual Keyboard That Not Only Has The Built In Phrases But Also Can Provide The Voice Notification/ Speech Assistance For The People Who Are Speech Disabled. To Achieve This We Have Used Our Pc/laptop Camera Which Is Built In And It Recognizes The Face And Parts Of The Face. This Makes The Process Of Detecting The Face Much Easier Than Anything. The Eye Blink Serves As The Alternative For A Mouse Click On The Virtual Interface. As Already Mentioned, Our Ultimate Achievement Is To Provide A Nonverbal Communication And Hence The Physically Disabled People Should Get A Mode Of Communication Along With A Voice Assistant. This Type Of Innovation Is A Golden Fortune For The People Who Lost Their Voice And Affected To Paralytic Disorders. We Have Further Explained With The Respective Flowcharts And With Each Juncture

CYBER THREAT DETECTION USING MACHINE LEARNING TECHNIQUES A PERFORMANCE EVALUATION PERSPECTIVE

The Present-day World Has Become All Dependent On Cyberspace For Every Aspect Of Daily Living. The Use Of Cyberspace Is Rising With Each Passing Day. The World Is Spending More Time On The Internet Than Ever Before. As A Result, The Risks Of Cyber Threats And Cybercrimes Are Increasing. The Term 'cyber Threat' Is Referred To As The Illegal Activity Performed Using The Internet. Cybercriminals Are Changing Their Techniques With Time To Pass Through The Wall Of Protection. Conventional Techniques Are Not Capable Of Detecting Zero-day Attacks And Sophisticated Attacks. Thus Far, Heaps Of Machine Learning Techniques Have Been Developed To Detect The Cybercrimes And Battle Against Cyber Threats. The Objective Of This Research Work Is To Present The Evaluation Of Some Of The Widely Used Machine Learning Techniques Used To Detect Some Of The Most Threatening Cyber Threats To The Cyberspace. Three Primary Machine Learning Techniques Are Mainly Investigated, Including Deep Belief Network, Decision Tree And Support Vector Machine. We Have Presented A Brief Exploration To Gauge The Performance Of These Machine Learning Techniques In The Spam Detection, Intrusion Detection And Malware Detection Based On Frequently Used And Benchmark Datasets.

DATA MANAGEMENT SERVICE

The Need For A Method To Create A Collaborative Machine Learning Model Which Can Utilize Data From Different Clients, Each With Privacy Constraints, Has Recently Emerged. This Is Due To Privacy Restrictions, Such As General Data Protection Regulation, Together With The Fact That Machine Learning Models In General Needs Large Size Data To Perform Well. Google Introduced Federated Learning In 2016 With The Aim To Address This Problem. Federated Learning Can Further Be Divided Into Horizontal And Vertical Federated Learning, Depending On How The Data Is Structured At The Different Clients. Vertical Federated Learning Is Applicable When Many Different Features Is Obtained On Distributed Computation Nodes, Where They Can Not Be Shared In Between. The Aim Of This Thesis Is To Identify The Current State Of The Art Methods In Vertical Federated Learning, Implement The Most Interesting Ones And Compare The Results In Order To Draw Conclusions Of The Benefits And Drawbacks Of The Different Methods. From The Results Of The Experiments, A Method Called FedBCD Shows Very Promising Results Where It Achieves Massive Improvements In The Number Of Communication Rounds Needed For Convergence, At The Cost Of More Computations At The Clients. A Comparison Between Synchronous And Asynchronous Approaches Shows Slightly Better Results For The Synchronous Approach In Scenarios With No Delay. Delay Refers To Slower Performance In One Of The Workers, Either Due To Lower Computational Resources Or Due To Communication Issues.

DECENTRALIZED MACHINE LEARNING AND APPLICATIONS

The Need For A Method To Create A Collaborative Machine Learning Model Which Can Utilize Data From Different Clients, Each With Privacy Constraints, Has Recently Emerged. This Is Due To Privacy Restrictions, Such As General Data Protection Regulation, Together With The Fact That Machine Learning Models In General Needs Large Size Data To Perform Well. Google Introduced Federated Learning In 2016 With The Aim To Address This Problem. Federated Learning Can Further Be Divided Into Horizontal And Vertical Federated Learning, Depending On How The Data Is Structured At The Different Clients. Vertical Federated Learning Is Applicable When Many Different Features Is Obtained On Distributed Computation Nodes, Where They Can Not Be Shared In Between. The Aim Of This Thesis Is To Identify The Current State Of The Art Methods In Vertical Federated Learning, Implement The Most Interesting Ones And Compare The Results In Order To Draw Conclusions Of The Benefits And Drawbacks Of The Different Methods. From The Results Of The Experiments, A Method Called FedBCD Shows Very Promising Results Where It Achieves Massive Improvements In The Number Of Communication Rounds Needed For Convergence, At The Cost Of More Computations At The Clients. A Comparison Between Synchronous And Asynchronous Approaches Shows Slightly Better Results For The Synchronous Approach In Scenarios With No Delay. Delay Refers To Slower Performance In One Of The Workers, Either Due To Lower Computational Resources Or Due To Communication Issues.

COVID-19 PANDEMIC- A Systematic Review On The Use Of AI And ML For Fighting The COVID-19 Pandemic

Artificial Intelligence (AI) And Machine Learning (ML) Have Caused A Paradigm Shift In Healthcare That Can Be Used For Decision Support And Forecasting By Exploring Medical Data. Recent Studies Have Shown That AI And ML Can Be Used To Fight COVID-19. The Objective Of This Article Is To Summarize The Recent AI- And ML-based Studies That Have Addressed The Pandemic. From An Initial Set Of 634 Articles, A Total Of 49 Articles Were Finally Selected Through An Inclusion-exclusion Process. In This Article, We Have Explored The Objectives Of The Existing Studies (i.e., The Role Of AI/ML In Fighting The COVID-19 Pandemic); The Context Of The Studies (i.e., Whether It Was Focused On A Specific Country-context Or With A Global Perspective; The Type And Volume Of The Dataset; And The Methodology, Algorithms, And Techniques Adopted In The Prediction Or Diagnosis Processes). We Have Mapped The Algorithms And Techniques With The Data Type By Highlighting Their Prediction/classification Accuracy. From Our Analysis, We Categorized The Objectives Of The Studies Into Four Groups: Disease Detection, Epidemic Forecasting, Sustainable Development, And Disease Diagnosis. We Observed That Most Of These Studies Used Deep Learning Algorithms On Image-data, More Specifically On Chest X-rays And CT Scans. We Have Identified Six Future Research Opportunities That We Have Summarized In This Paper.

FACE EXPRESSION RECOGNITION- Automatic Detection Of Pain From Facial Expressions A Survey

Pain Sensation Is Essential For Survival, Since It Draws Attention To Physical Threat To The Body. Pain Assessment Is Usually Done Through Self-reports. However, Self-assessment Of Pain Is Not Available In The Case Of Noncommunicative Patients, And Therefore, Observer Reports Should Be Relied Upon. Observer Reports Of Pain Could Be Prone To Errors Due To Subjective Biases Of Observers. Moreover, Continuous Monitoring By Humans Is Impractical. Therefore, Automatic Pain Detection Technology Could Be Deployed To Assist Human Caregivers And Complement Their Service, Thereby Improving The Quality Of Pain Management, Especially For Noncommunicative Patients. Facial Expressions Are A Reliable Indicator Of Pain, And Are Used In All Observer-based Pain Assessment Tools. Following The Advancements In Automatic Facial Expression Analysis, Computer Vision Researchers Have Tried To Use This Technology For Developing Approaches For Automatically Detecting Pain From Facial Expressions. This Paper Surveys The Literature Published In This Field Over The Past Decade, Categorizes It, And Identifies Future Research Directions. The Survey Covers The Pain Datasets Used In The Reviewed Literature, The Learning Tasks Targeted By The Approaches, The Features Extracted From Images And Image Sequences To Represent Pain-related Information, And Finally, The Machine Learning Methods Used.

CREDIT CARD FRAUDULET TRANSACTION DETECTION- Supervised Machine Learning Algorithm For Credit Card Fraudulent Transaction Detection

The Goal Of Data Analytics Is To Delineate Hidden Patterns And Use Them To Support Informed Decisions In A Variety Of Situations. Credit Card Fraud Is Escalating Significantly With The Advancement Of The Modernized Technology And Become An Easy Target For Fraudulent. Credit Card Fraud Is A Severe Problem In The Financial Service And Costs Billions Of A Dollar Every Year. The Design Of Fraud Detection Algorithm Is A Challenging Task With The Lack Of Real-world Transaction Dataset Because Of Confidentiality And The Highly Imbalanced Publicly Available Datasets. In This Paper, We Apply Different Supervised Machine Learning Algorithms To Detect Credit Card Fraudulent Transaction Using A Real-world Dataset. Furthermore, We Employ These Algorithms To Implement A Super Classifier Using Ensemble Learning Methods. We Identify The Most Important Variables That May Lead To Higher Accuracy In Credit Card Fraudulent Transaction Detection. Additionally, We Compare And Discuss The Performance Of Various Supervised Machine Learning Algorithms Exist In Literature Against The Super Classifier That We Implemented In This Paper.

EHEALTHCARE RECOMMENDATION SCHEME- PPMR A Privacy-preserving Online Medical Service Recommendation Scheme In EHealthcare System

With The Continuous Development Of EHealthcare Systems, Medical Service Recommendation Has Received Great Attention. However, Although It Can Recommend Doctors To Users, There Are Still Challenges In Ensuring The Accuracy And Privacy Of Recommendation. In This Paper, To Ensure The Accuracy Of The Recommendation, We Consider Doctors' Reputation Scores And Similarities Between Users' Demands And Doctors' Information As The Basis Of The Medical Service Recommendation. The Doctors' Reputation Scores Are Measured By Multiple Feedbacks From Users. We Propose Two Concrete Algorithms To Compute The Similarity And The Reputation Scores In A Privacy-preserving Way Based On The Modified Paillier Cryptosystem, Truth Discovery Technology, And The Dirichlet Distribution. Detailed Security Analysis Is Given To Show Its Security Prosperities. In Addition, Extensive Experiments Demonstrate The Efficiency In Terms Of Computational Time For Truth Discovery And Recommendation Process.

FAULT TOLERANT DATA PROCESSING IN HEALTHCARE- Adaptive And Fault-tolerant Data Processing In Healthcare IoT Based On Fog Computing Networks

In Recent Years, Healthcare IoT Have Been Helpful In Mitigating Pressures Of Hospital And Medical Resources Caused By Aging Population To A Large Extent. As A Safety-critical System, The Rapid Response From The Health Care System Is Extremely Important. To Fulfill The Low Latency Requirement, Fog Computing Is A Competitive Solution By Deploying Healthcare IoT Devices On The Edge Of Clouds. However, These Fog Devices Generate Huge Amount Of Sensor Data. Designing A Specific Framework For Fog Devices To Ensure Reliable Data Transmission And Rapid Data Processing Becomes A Topic Of Utmost Significance. In This Paper, A Reduced Variable Neighborhood Search (RVNS)-based SEnsor Data Processing Framework (REDPF) Is Proposed To Enhance Reliability Of Data Transmission And Processing Speed. Functionalities Of REDPF Include Fault-tolerant Data Transmission, Self-adaptive Filtering And Data-load-reduction Processing. Specifically, A Reliable Transmission Mechanism, Managed By A Self-adaptive Filter, Will Recollect Lost Or Inaccurate Data Automatically. Then, A New Scheme Is Designed To Evaluate The Health Status Of The Elderly People. Through Extensive Simulations, We Show That Our Proposed Scheme Improves Network Reliability, And Provides A Faster Processing Speed.

REMOTE AUTHENTICATION SCHEMES- Remote Authentication Schemes For Wireless Body Area Networks Based On The Internet Of Things

Internet Of Things (IoT) Is A New Technology Which Offers Enormous Applications That Make People’s Lives More Convenient And Enhances Cities’ Development. In Particular, Smart Healthcare Applications In IoT Have Been Receiving Increasing Attention For Industrial And Academic Research. However, Due To The Sensitiveness Of Medical Information, Security And Privacy Issues In IoT Healthcare Systems Are Very Important. Designing An Efficient Secure Scheme With Less Computation Time And Energy Consumption Is A Critical Challenge In IoT Healthcare Systems. In This Paper, A Lightweight Online/offline Certificateless Signature (L-OOCLS) Is Proposed, Then A Heterogeneous Remote Anonymous Authentication Protocol (HRAAP) Is Designed To Enable Remote Wireless Body Area Networks (WBANs) Users To Anonymously Enjoy Healthcare Service Based On The IoT Applications. The Proposed L-OOCLS Scheme Is Proven Secure In Random Oracle Model And The Proposed HRAAP Can Resist Various Types Of Attacks. Compared With The Existing Relevant Schemes, The Proposed HRAAP Achieves Less Computation Overhead As Well As Less Power Consumption On WBANs Client. In Addition, To Nicely Meet The Application In The IoT, An Application Scenario Is Given.

ATTRIBUTE BASED SIGNATURE- Outsourced Decentralized Multi-Authority Attribute Based Signature And Its Application In Iot

IoT (Internet Of Things) Devices Often Collect Data And Store The Data In The Cloud For Sharing And Further Processing; This Collection, Sharing, And Processing Will Inevitably Encounter Secure Access And Authentication Issues. Attribute Based Signature (ABS), Which Utilizes The Signer’s Attributes To Generate Private Keys, Plays A Competent Role In Data Authentication And Identity Privacy Preservation. In ABS, There Are Multiple Authorities That Issue Different Private Keys For Signers Based On Their Various Attributes, And A Central Authority Is Usually Established To Manage All These Attribute Authorities. However, One Security Concern Is That If The Central Authority Is Compromised, The Whole System Will Be Broken. In This Paper, We Present An Outsourced Decentralized Multi-authority Attribute Based Signature (ODMA-ABS) Scheme. The Proposed ODMA-ABS Achieves Attribute Privacy And Stronger Authority-corruption Resistance Than Existing Multi-authority Attribute Based Signature Schemes Can Achieve. In Addition, The Overhead To Generate A Signature Is Further Reduced By Outsourcing Expensive Computation To A Signing Cloud Server. We Present Extensive Security Analysis And Experimental Simulation Of The Proposed Scheme. We Also Propose An Access Control Scheme That Is Based On ODMA-ABS.

TO ENGAGE STUDENT IN SOCIAL MEDIA NETWORK- Creating And Using Digital Games For Learning In Elementary And Secondary Education

The Use Of Digital Games In Education Has Gained Considerable Popularity In The Last Years Due To The Fact That These Games Are Considered To Be Excellent Tools For Teaching And Learning And Offer To Students An Engaging And Interesting Way Of Participating And Learning. In This Study, The Design And Implementation Of Educational Activities That Include Game Creation And Use In Elementary And Secondary Education Is Presented. The Proposed Educational Activities’ Content Covers The Parts Of The Curricula Of All The Informatics Courses, For Each Education Level Separately, That Include The Learning Of Programming Principles. The Educational Activities Were Implemented And Evaluated By Teachers Through A Discussion Session. The Findings Indicate That The Teachers Think That Learning Through Creating And Using Games Is More Interesting And That They Also Like The Idea Of Using Various Programming Environments To Create Games In Order To Teach Basic Programming Principles To Students.

DRIMUX Dynamic Rumor Influence Minimization With User Experience In Social Networks

With The Soaring Development Of Large Scale Online Social Networks, Online Information Sharing Is Becoming Ubiquitous Every Day. Various Information Is Propagating Through Online Social Networks Including Both The Positive And Negative. In This Paper, We Focus On The Negative Information Problems Such As The Online Rumors. With The Soaring Development Of Large Scale Online Social Networks, Online Information Sharing Is Becoming Ubiquitous Everyday. Various Information Is Propagating Through Online Social Networks Including Both The Positive And Negative. In This Paper, We Focus On The Negative Information Problems Such As The Online Rumors. Rumor Blocking Is A Serious Problem In Large-scale Social Networks. Malicious Rumors Could Cause Chaos In Society And Hence Need To Be Blocked As Soon As Possible After Being Detected. In This Paper, We Propose A Model Of Dynamic Rumor Influence Minimization With User Experience (DRIMUX). Our Goal Is To Minimize The Influence Of The Rumor (i.e., The Number Of Users That Have Accepted And Sent The Rumor) By Blocking A Certain Subset Of Nodes. A Dynamic Ising Propagation Model Considering Both The Global Popularity And Individual Attraction Of The Rumor Is Presented Based On A Realistic Scenario. In Addition, Different From Existing Problems Of Influence Minimization, We Take Into Account The Constraint Of User Experience Utility. Specifically, Each Node Is Assigned A Tolerance Time Threshold. If The Blocking Time Of Each User Exceeds That Threshold, The Utility Of The Network Will Decrease. Under This Constraint, We Then Formulate The Problem As A Network Inference Problem With Survival Theory, And Propose Solutions Based On Maximum Likelihood Principle. Experiments Are Implemented Based On Large-scale Real World Networks And Validate The Effectiveness Of Our Method.

USER PROFILE MATCHING- Privacy-Preserving User Profile Matching In Social Networks

In This Paper, We Consider A Scenario Where A User Queries A User Profile Database, Maintained By A Social Networking Service Provider, To Identify Users Whose Profiles Match The Profile Specified By The Querying User. A Typical Example Of This Application Is Online Dating. Most Recently, An Online Dating Website, Ashley Madison, Was Hacked, Which Resulted In A Disclosure Of A Large Number Of Dating User Profiles. This Data Breach Has Urged Researchers To Explore Practical Privacy Protection For User Profiles In A Social Network. In This Paper, We Propose A Privacy-preserving Solution For Profile Matching In Social Networks By Using Multiple Servers. Our Solution Is Built On Homomorphic Encryption And Allows A User To Find Out Matching Users With The Help Of Multiple Servers Without Revealing To Anyone The Query And The Queried User Profiles In Clear. Our Solution Achieves User Profile Privacy And User Query Privacy As Long As At Least One Of The Multiple Servers Is Honest. Our Experiments Demonstrate That Our Solution Is Practical.

PHARMACOVIGILANCE FROM SOCIAL MEDIA- Pharmacovigilance From Social Media An Improved Random Subspace Method For Identifying Adverse Drug Events

Social Media-based Pharmacovigilance Has Great Potential To Augment Current Efforts And Provide Regulatory Authorities With Valuable Decision Aids. Among Various Pharmacovigilance Activities, Identifying Adverse Drug Events (ADEs) Is Very Important For Patient Safety. However, In Health-related Discussion Forums, ADEs May Confound With Drug Indications And Beneficial Effects, Etc. Therefore, The Focus Of This Study Is To Develop A Strategy To Identify ADEs From Other Semantic Types, And Meanwhile To Determine The Drug That An ADE Is Associated With. And Then Get The Id Of An User Who Share The ADE On The Medical Social Media. The User Id Detect By Using Naïve Bayes Algorithm.

OWNERSHIP IDENTIFICATION AND SIGNALING OF MULTIMEDIA- Security Of Multimedia Content For Ownership Identification Using Signaling Technique

The Information Shared Over Network Like Audio And Video Files Will Be Having Major Challenge Due To Security Credentials. In Large Scale Systems Like Cloud Infrastructure Used To Improve The Better Security From Past One Decade. The Contents Are Like Pictures, Audio And Video Clips Are Shared Over The Online Training Sessions In Recent Days. Therefore The Video Files Are Used To Protect Using Digital Signature And Digital Watermarking. The Content Of The Multimedia Files Are Require The Better Environment For Sharing Of Knowledge Using Private And Public Clouds. The Digital Signature Method Is Used For Multimedia Components Such As 2D And 3D Video Clips And Shared Among The Users On Cloud Infrastructure Will Be Predicted With Various Cloud Based Security Techniques.

PRIVACY-PRESERVING MULTI-KEYWORD RANKED SEARCH- Enhanced Semantic-Aware Multi-Keyword Ranked Search Scheme Over Encrypted

Traditional Searchable Encryption Schemes Based On The Term Frequency-Inverse Document Frequency (TF-IDF) Model Adopt The Presence Of Keywords To Measure The Relevance Of Documents To Queries, Which Ignores The Latent Semantic Meanings That Are Concealed In The Context. Latent Dirichlet Allocation (LDA) Topic Model Can Be Utilized For Modeling The Semantics Among Texts To Achieve Semantic-aware Multi-keyword Search. However, The LDA Topic Model Treats Queries And Documents From The Perspective Of Topics, And The Keywords Information Is Ignored. In This Paper, We Propose A Privacy-preserving Searchable Encryption Scheme Based On The LDA Topic Model And The Query Likelihood Model. We Extract The Feature Keywords From The Document Using The LDA-based Information Gain (IG) And Topic Frequency-Inverse Topic Frequency (TF-ITF) Model. With Feature Keyword Extraction And The Query Likelihood Model, Our Scheme Can Achieve A More Accurate Semantic-aware Keyword Search. A Special Index Tree Is Used To Enhance Search Efficiency. The Secure Inner Product Operation Is Utilized To Implement The Privacy-preserving Ranked Search. The Experiments On Real-world Datasets Demonstrate The Effectiveness Of Our Scheme.

SECURE SHARING OF PESONAL HEALTH RECORDS IN TH CLOUD- Secure Outsourced Attribute-based Sharing Framework For Lightweight Devices In Smart Health Systems

Personal Health Record (PHR) Service Is An Emerging Model For Health Information Exchange. It Allows Patients To Create, Update And Manage Personal And Medical Information. Also They Can Control And Share Their Medical Information With Other Users As Well As Health Care Providers. PHR Data Is Hosted To The Third Party Cloud Service Providers In Order To Enhance Its Interoperability. However, There Have Been Serious Security And Privacy Issues In Outsourcing These Data To Cloud Server. For Security, Encrypt The PHRs Before Outsourcing. So Many Issues Such As Risks Of Privacy Exposure, Scalability In Key Management, Flexible Access And Efficient User Revocation, Have Remained The Most Important Challenges Toward Achieving Fine-grained, Cryptographically Enforced Data Access Control. To Achieve Fine-grained And Scalable Data Access Control For Client’s Data, A Novel Patient-centric Framework Is Used. This Frame Work Is Mainly Focus On The Multiple Data Owner Scenario. A High Degree Of Patient Privacy Is Guaranteed Simultaneously By Exploiting Multi Authority ABE. This Scheme Also Enables Dynamic Modification Of Access Policies Or File Attributes, Support Efficient On Demand User/attribute Revocation. However Some Practical Limitations Are In Building PHR System. If Consider The Workflow Based Access Control Scenarios, The Data Access Right Could Be Given Based On Users Identities Rather Than Their Attributes, While ABE Does Not Handle That Efficiently. For Solving These Problem In This Thesis Proposed PHR System, Based On Attribute Based Broadcast Encryption (ABBE).

ATTRIBUTE-BASED ACCESS CONTROL WITH CONSTANT-SIZE CIPHERTEXT- Efficient Multi-Authority Attribute-Based Signcryption With Constant-Size Cipher Text

Recently, Efficient Fine-grained Access Mechanism Has Been Studied As A Main Concern In Cloud Storage Area For Several Years. Attribute-based Signcryption (ABSC) Which Is Logical Combination Of Attribute-based Encryption(ABE) And Attribute-based Signature(ABS), Can Provide Confidentiality, Authenticity For Sensitive Data And Anonymous Authentication. At The Same Time It Is More Efficient Than Previous “encrypt-then-sign” And “sign-then-encrypt” Patterns. However, Most Of The Existing ABSC Schemes Fail To Serve For Real Scenario Of Multiple Authorities And Have Heavy Communication Overhead And Computing Overhead. Hence, We Construct A Novel ABSC Scheme Realizing Multi-authority Access Control And Constant-size Ciphertext That Does Not Depend On The Number Of Attributes Or Authorities. Furthermore, Our Scheme Provides Public Verifiability Of The Ciphertext And Privacy Protection For The Signcryptor. Specially, It Is Proven To Be Secure In The Standard Model, Including Ciphertext Indistinguishability Under Adaptive Chosen Ciphertext Attacks And Existential Unforgeability Under Adaptive Chosen Message Attack.

ENCRYTION CLOUD BASED REVOCATION - Efficient Revocable Multi-Authority Attribute-Based Encryption For Cloud Storage

As Is Known, Attribute-based Encryption (ABE) Is Usually Adopted For Cloud Storage, Both For Its Achievement Of Fine-grained Access Control Over Data, And For Its Guarantee Of Data Confidentiality. Nevertheless, Single-authority Attribute-based Encryption (SA-ABE) Has Its Obvious Drawback In That Only One Attribute Authority Can Assign The Users' Attributes, Enabling The Data To Be Shared Only Within The Management Domain Of The Attribute Authority, While Rendering Multiple Attribute Authorities Unable To Share The Data. On The Other Hand, Multi-authority Attribute-based Encryption (MA-ABE) Has Its Advantages Over SA-ABE. It Can Not Only Satisfy The Need For The Fine-grained Access Control And Confidentiality Of Data, But Also Make The Data Shared Among Different Multiple Attribute Authorities. However, Existing MA-ABE Schemes Are Unsuitable For The Devices With Resources-constraint, Because These Schemes Are All Based On Expensive Bilinear Pairing. Moreover, The Major Challenge Of MA-ABE Scheme Is Attribute Revocation. So Far, Many Solutions In This Respect Are Not Efficient Enough. In This Paper, On The Basis Of The Elliptic Curves Cryptography, We Propose An Efficient Revocable Multi-authority Attribute-based Encryption (RMA-ABE) Scheme For Cloud Storage. The Security Analysis Indicates That The Proposed Scheme Satisfies Indistinguishable Under Adaptive Chosen Plaintext Attack Assuming Hardness Of The Decisional Diffie-Hellman Problem. Compared With The Other Schemes, The Proposed Scheme Gets Its Advantages In That It Is More Economical In Computation And Storage.

DIPLOCLOUD- DistSim - Scalable Distributed In-Memory Semantic Similarity Estimation For RDF Knowledge Graphs

In This Paper, We Present DistSim, A Scalable Distributed In-Memory Semantic Similarity Estimation Framework For Knowledge Graphs. DistSim Provides A Multitude Of State-ofthe-art Similarity Estimators. We Have Developed The Similarity Estimation Pipeline By Combining Generic Software Modules. For Large Scale RDF Data, DistSim Proposes MinHash With Locality Sensitivity Hashing To Achieve Better Scalability Over All-pair Similarity Estimations. The Modules Of DistSim Can Be Set Up Using A Multitude Of (hyper)-parameters Allowing To Adjust The Tradeoff Between Information Taken Into Account, And Processing Time. Furthermore, The Output Of The Similarity Estimation Pipeline Is Native RDF. DistSim Is Integrated Into The SANSA Stack, Documented In Scala-docs, And Covered By Unit Tests. Additionally, The Variables And Provided Methods Follow The Apache Spark MLlib Name-space Conventions. The Performance Of DistSim Was Tested Over A Distributed Cluster, For The Dimensions Of Data Set Size And Processing Power Versus Processing Time, Which Shows The Scalability Of DistSim W.r.t. Increasing Data Set Sizes And Processing Power. DistSim Is Already In Use For Solving Several RDF Data Analytics Related Use Cases. Additionally, DistSim Is Available And Integrated Into The Open-source GitHub Project SANSA

SECURE ANTI-COLLUSION DATA SHARING SCHEME - Attribute-Based Keyword Search Encryption Scheme With Verifiable Ciphertext Via Blockchain

In Order To Realize The Sharing Of Data By Multiple Users On The Blockchain, This Paper Proposes An Attribute-based Searchable Encryption With Verifiable Ciphertext Scheme Via Blockchain. The Scheme Uses The Public Key Algorithm To Encrypt The Keyword, The Attribute-based Encryption Algorithm To Encrypt The Symmetric Key, And The Symmetric Key To Encrypt The File. The Keyword Index Is Stored On The Blockchain, And The Ciphertext Of The Symmetric Key And File Are Stored On The Cloud Server. The Scheme Uses Searchable Encryption Technology To Achieve Secure Search On The Blockchain, Uses The Immutability Of The Blockchain To Ensure The Security Of The Keyword Ciphertext, Uses Verify Algorithm Guarantees The Integrity Of The Data On The Cloud. When The User's Attributes Need To Be Changed Or The Ciphertext Access Structure Is Changed, The Scheme Uses Proxy Re-encryption Technology To Implement The User's Attribute Revocation, And The Authority Center Is Responsible For The Whole Attribute Revocation Process. The Security Proof Shows That The Scheme Can Achieve Ciphertext Security, Keyword Security And Anti-collusion. In Addition, The Numerical Results Show That The Proposed Scheme Is Effective.

SHARED DYNAMIC CLOUD DATA WITH GROUP USER REVOCATION- Secure Efficient Revocable Large Universe Multi-Authority Attribute-Based Encryption For Cloud-Aided IoT

With The Help Of Cloud Computing, The Ubiquitous And Diversified Internet Of Things (IoT) Has Greatly Improved Human Society. Revocable Multi-authority Attribute-based Encryption (MA-ABE) Is Considered A Promising Technique To Solve The Security Challenges On Data Access Control In The Dynamic IoT Since It Can Achieve Dynamic Access Control Over The Encrypted Data. However, On The One Hand, The Existing Revocable Large Universe MA-ABE Suffers The Collusion Attack Launched By Revoked Users And Non-revoked Users. On The Other Hand, The User Collusion Avoidance Revocable MA-ABE Schemes Do Not Support Large Attributes (or Users) Universe, I.e. The Flexible Number Of Attributes (or Users). In This Article, The Author Proposes An Efficient Revocable Large Universe MA-ABE Based On Prime Order Bilinear Groups. The Proposed Scheme Supports User-attribute Revocation, I.e., The Revoked User Only Loses One Or More Attributes, And She/he Can Access The Data So Long As Her/his Remaining Attributes Satisfy The Access Policy. It Is Static Security In The Random Oracle Model Under The Q-DPBDHE2 Assumption. Moreover, It Is Secure Against The Collusion Attack Launched By Revoked Users And Non-revoked Users. Meanwhile, It Meets The Requirements Of Forward And Backward Security. The Limited-resource Users Can Choose Outsourcing Decryption To Save Resources. The Performance Analysis Results Indicate That It Is Suitable For Large-scale Cross-domain Collaboration In The Dynamic Cloud-aided IoT.

PROFIT MAXIMIZATION SCHEME- Personality- And Value-aware Scheduling Of User Requests In Cloud For Profit Maximization

The Main Goal Of A Cloud Provider Is To Make Profits By Providing Services To Users. Existing Profit Optimization Strategies Employ Homogeneous User Models In Which User Personality Is Ignored, Resulting In Fewer Profits And Particularly Notably Lower User Satisfaction That In Turn, Leads To Fewer Users And Reduced Profits. In This Paper, We Propose Efficient Personality-aware Request Scheduling Schemes To Maximize The Profit Of The Cloud Provider Under The Constraint Of User Satisfaction. Specifically, We First Model The Service Requests At The Granularity Of Individual Personality And Propose A Personalized User Satisfaction Prediction Model Based On Questionnaires. Subsequently, We Design A Personality-guided Integer Linear Programming (ILP)-based Request Scheduling Algorithm To Maximize The Profit Under The Constraint Of User Satisfaction, Which Is Followed By An Approximate But Lightweight Value Assessment And Cross Entropy (VACE)-based Profit Improvement Scheme. The VACE-based Scheme Is Especially Tailored For Applications With High Scheduling Resolution. Extensive Simulation Results Show That Our Satisfaction Prediction Model Can Achieve The Accuracy Of Up To 83%, And Our Profit Optimization Schemes Can Improve The Profit By At Least 3.96% As Compared To The Benchmarking Methods While Still Obtaining A Speedup Of At Least 1.68x

MULTIMEDIA CONTENT PRTECTION SYSTEM- Cloud-Based Multimedia Content Protection System

We Propose A New Design For Large-scale Multimedia Content Protection Systems. Our Design Leverages Cloud Infrastructures To Provide Cost Efficiency, Rapid Deployment, Scalability, And Elasticity To Accommodate Varying Workloads. The Proposed System Can Be Used To Protect Different Multimedia Content Types, Including Videos, Images, Audio Clips, Songs, And Music Clips. The System Can Be Deployed On Private And/or Public Clouds. Our System Has Two Novel Components: (i) Method To Create Signatures Of Videos, And (ii) Distributed Matching Engine For Multimedia Objects. The Signature Method Creates Robust And Representative Signatures Of Videos That Capture The Depth Signals In These Videos And It Is Computationally Efficient To Compute And Compare As Well As It Requires Small Storage. The Distributed Matching Engine Achieves High Scalability And It Is Designed To Support Different Multimedia Objects. We Implemented The Proposed System And Deployed It On Two Clouds: Amazon Cloud And Our Private Cloud. Our Experiments With More Than 11,000 Videos And 1 Million Images Show The High Accuracy And Scalability Of The Proposed System. In Addition, We Compared Our System To The Protection System Used By YouTube And Our Results Show That The YouTube Protection System Fails To Detect Most Copies Of Videos, While Our System Detects More Than 98% Of Them.

PUBLISHING OF SET-VALUED DATA ON HYBRID CLOUD- A Privacy Preserving Method For Publishing Set Valued Data And Its Correlative Social Network

Set-valued Data And Social Network Provide Opportunities To Mine Useful, Yet Potentially Security-sensitive, Information. While There Are Mechanisms To Anonymize Data And Protect The Privacy Separately In Set-valued Data And In Social Network, The Existing Approaches In Data Privacy Do Not Address The Privacy Issue Which Emerge When Publishing Set-valued Data And Its Correlative Social Network Simultaneously. In This Paper, We Propose A Privacy Attack Model Based On Linking The Set-valued Data And The Social Network Topology Information And A Novel Technique To Defend Against Such Attack To Protect The Individual Privacy. To Improve Data Utility And The Practicality Of Our Scheme, We Use Local Generalization And Partial Suppression To Make Set-valued Data Satisfy The Grouped ρ-uncertainty Model And To Reduce The Impact On The Community Structure Of The Social Network When Anonymizing The Social Network. Experiments On Real-life Data Sets Show That Our Method Outperforms The Existing Mechanisms In Data Privacy And, More Specifically, That It Provides Greater Data Utility While Having Less Impact On The Community Structure Of Social Networks.

DECENTRALIZED CLOUD FIREWALL FRAMEWORK Hierarchical Multi-Agent Optimization For Resource Allocation In Cloud Computing

In Cloud Computing, An Important Concern Is To Allocate The Available Resources Of Service Nodes To The Requested Tasks On Demand And To Make The Objective Function Optimum, I.e., Maximizing Resource Utilization, Payoffs, And Available Bandwidth. This Article Proposes A Hierarchical Multi-agent Optimization (HMAO) Algorithm In Order To Maximize The Resource Utilization And Make The Bandwidth Cost Minimum For Cloud Computing. The Proposed HMAO Algorithm Is A Combination Of The Genetic Algorithm (GA) And The Multi-agent Optimization (MAO) Algorithm. With Maximizing The Resource Utilization, An Improved GA Is Implemented To Find A Set Of Service Nodes That Are Used To Deploy The Requested Tasks. A Decentralized-based MAO Algorithm Is Presented To Minimize The Bandwidth Cost. We Study The Effect Of Key Parameters Of The HMAO Algorithm By The Taguchi Method And Evaluate The Performance Results. The Results Demonstrate That The HMAO Algorithm Is More Effective Than Two Baseline Algorithms Of Genetic Algorithm (GA) And Fast Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) In Solving The Large-scale Optimization Problem Of Resource Allocation. Furthermore, We Provide The Performance Comparison Of The HMAO Algorithm With Two Heuristic Greedy And Viterbi Algorithms In On-line Resource Allocation.

CRYPTOGRAPIC ALGORITHM- Comparative Study Of Cryptographic Algorithm

We Know That With The Emergence Of Internet People All Around The World Are Using Its Services &are Heavily Dependent On It. People Are Also Storing Their Huge Amount Of Data Over The Cloud .It Is The Challenge For Researchers To Secure The Private And Critical Data Of The Users, So That Unauthorized Person Should Not Be Able To Access It And Manipulate It .Cryptography Is A Process Of Converting The User Useful Information To A Form Which Is Insignificant To An Unauthorized Person So That Only Authorized Persons Can Access And Understands It .For Ensuring Privacy There Are Multiple Cryptographic Algorithms, Which Is Selected As Per Requirement Of User Or Security Specification Of The Organization. This Paper Discusses The Comparison Of Various Cryptographic Encryption Algorithms With Respect To Its Various Key Features & Then Later Discusses Their Performance Cost Based On The Some Selected Key Criteria’s. Some Of The Algorithms Chosen For The Purpose Are DES, 3DES, IDEA, CAST128, AES, Blowfish, RSA, ABE &ECC.

DATA INTEGRITY AUDITING WITHOUT PRIVATE KEY - Data Integrity Auditing Without Private Key Storage For Secure Cloud Storage

Using Cloud Storage Services, Users Can Store Their Data In The Cloud To Avoid The Expenditure Of Local Data Storage And Maintenance. To Ensure The Integrity Of The Data Stored In The Cloud, Many Data Integrity Auditing Schemes Have Been Proposed. In Most, If Not All, Of The Existing Schemes, A User Needs To Employ His Private Key To Generate The Data Authenticators For Realizing The Data Integrity Auditing. Thus, The User Has To Possess A Hardware Token (e.g., USB Token, Smart Card) To Store His Private Key And Memorize A Password To Activate This Private Key. If This Hardware Token Is Lost Or This Password Is Forgotten, Most Of The Current Data Integrity Auditing Schemes Would Be Unable To Work. In Order To Overcome This Problem, We Propose A New Paradigm Called Data Integrity Auditing Without Private Key Storage And Design Such A Scheme. In This Scheme, We Use Biometric Data (e.g., Iris Scan, Fingerprint) As The User’s Fuzzy Private Key To Avoid Using The Hardware Token. Meanwhile, The Scheme Can Still Effectively Complete The Data Integrity Auditing. We Utilize A Linear Sketch With Coding And Error Correction Processes To Confirm The Identity Of The User. In Addition, We Design A New Signature Scheme Which Not Only Supports Blockless Verifiability, But Also Is Compatible With The Linear Sketch. The Security Proof And The Performance Analysis Show That Our Proposed Scheme Achieves Desirable Security And Efficiency.

PUBLIC-KEY ENCRYPTION WITH KEYWORD SEARCH- Dual-Server Public-Key Encryption With Keyword Search For Secure Cloud Storage

Searchable Encryption Is Of Increasing Interest For Protecting The Data Privacy In Secure Searchable Cloud Storage. In This Paper, We Investigate The Security Of A Well-known Cryptographic Primitive, Namely, Public Key Encryption With Keyword Search (PEKS) Which Is Very Useful In Many Applications Of Cloud Storage. Unfortunately, It Has Been Shown That The Traditional PEKS Framework Suffers From An Inherent Insecurity Called Inside Keyword Guessing Attack (KGA) Launched By The Malicious Server. To Address This Security Vulnerability, We Propose A New PEKS Framework Named Dual-server PEKS (DS-PEKS). As Another Main Contribution, We Define A New Variant Of The Smooth Projective Hash Functions (SPHFs) Referred To As Linear And Homomorphic SPHF (LH-SPHF). We Then Show A Generic Construction Of Secure DS-PEKS From LH-SPHF. To Illustrate The Feasibility Of Our New Framework, We Provide An Efficient Instantiation Of The General Framework From A Decision Diffie-Hellman-based LH-SPHF And Show That It Can Achieve The Strong Security Against Inside The KGA.

DEDUPLICATING DATA- Secure Auditing And Deduplicating Data In Cloud

As The Cloud Computing Technology Develops During The Last Decade, Outsourcing Data To Cloud Service For Storage Becomes An Attractive Trend, Which Benefits In Sparing Efforts On Heavy Data Maintenance And Management. Nevertheless, Since The Outsourced Cloud Storage Is Not Fully Trustworthy, It Raises Security Concerns On How To Realize Data Deduplication In Cloud While Achieving Integrity Auditing. In This Work, We Study The Problem Of Integrity Auditing And Secure Deduplication On Cloud Data. Specifically, Aiming At Achieving Both Data Integrity And Deduplication In Cloud, We Propose Two Secure Systems, Namely SecCloud And SecCloud $^+$ . SecCloud Introduces An Auditing Entity With A Maintenance Of A MapReduce Cloud, Which Helps Clients Generate Data Tags Before Uploading As Well As Audit The Integrity Of Data Having Been Stored In Cloud. Compared With Previous Work, The Computation By User In SecCloud Is Greatly Reduced During The File Uploading And Auditing Phases. SecCloud $^+$ Is Designed Motivated By The Fact That Customers Always Want To Encrypt Their Data Before Uploading, And Enables Integrity Auditing And Secure Deduplication On Encrypted Data.

AUDITING FOR OUTSOURCED DATABASE- Verifiable Auditing For Outsourced Database In Cloud Computing

The Notion Of Database Outsourcing Enables The Data Owner To Delegate The Database Management To A Cloud Service Provider (CSP) That Provides Various Database Services To Different Users. Recently, Plenty Of Research Work Has Been Done On The Primitive Of Outsourced Database. However, It Seems That No Existing Solutions Can Perfectly Support The Properties Of Both Correctness And Completeness For The Query Results, Especially In The Case When The Dishonest CSP Intentionally Returns An Empty Set For The Query Request Of The User. In This Paper, We Propose A New Verifiable Auditing Scheme For Outsourced Database, Which Can Simultaneously Achieve The Correctness And Completeness Of Search Results Even If The Dishonest CSP Purposely Returns An Empty Set. Furthermore, We Can Prove That Our Construction Can Achieve The Desired Security Properties Even In The Encrypted Outsourced Database. Besides, The Proposed Scheme Can Be Extended To Support The Dynamic Database Setting By Incorporating The Notion Of Verifiable Database With Updates.

ATTRIBUTE-BASED ENCRYPTION- Audit-Free Cloud Storage Via Deniable Attribute-based Encryption

Cloud Storage Services Have Become Increasingly Popular. Because Of The Importance Of Privacy, Many Cloud Storage Encryption Schemes Have Been Proposed To Protect Data From Those Who Do Not Have Access. All Such Schemes Assumed That Cloud Storage Providers Are Safe And Cannot Be Hacked; However, In Practice, Some Authorities (i.e., Coercers) May Force Cloud Storage Providers To Reveal User Secrets Or Confidential Data On The Cloud, Thus Altogether Circumventing Storage Encryption Schemes. In This Paper, We Present Our Design For A New Cloud Storage Encryption Scheme That Enables Cloud Storage Providers To Create Convincing Fake User Secrets To Protect User Privacy. Since Coercers Cannot Tell If Obtained Secrets Are True Or Not, The Cloud Storage Providers Ensure That User Privacy Is Still Securely Protected.

PROOF OF RETRIEVABILITY IN CLOUD COMPUTING- Enabling Proof Of Retrievability In Cloud Computing With Resource-Constrained Devices

Cloud Computing Moves The Application Software And Databases To The Centralized Large Data Centers, Where The Management Of The Data And Services May Not Be Fully Trustworthy. In This Work, We Study The Problem Of Ensuring The Integrity Of Data Storage In Cloud Computing. To Reduce The Computational Cost At User Side During The Integrity Verification Of Their Data, The Notion Of Public Verifiability Has Been Proposed. However, The Challenge Is That The Computational Burden Is Too Huge For The Users With Resource-constrained Devices To Compute The Public Authentication Tags Of File Blocks. To Tackle The Challenge, We Propose OPoR, A New Cloud Storage Scheme Involving A Cloud Storage Server And A Cloud Audit Server, Where The Latter Is Assumed To Be Semi-honest. In Particular, We Consider The Task Of Allowing The Cloud Audit Server, On Behalf Of The Cloud Users, To Pre-process The Data Before Uploading To The Cloud Storage Server And Later Verifying The Data Integrity. OPoR Outsources And Offloads The Heavy Computation Of The Tag Generation To The Cloud Audit Server And Eliminates The Involvement Of User In The Auditing And In The Pre-processing Phases. Furthermore, We Strengthen The Proof Of Retrievability (PoR) Model To Support Dynamic Data Operations, As Well As Ensure Security Against Reset Attacks Launched By The Cloud Storage Server In The Upload Phase.

DYNAMIC DATA POSSESSION IN CLOUD COMPUTING- Provable Multicopy Dynamic Data Possession In Cloud Computing Systems

Increasingly More And More Organizations Are Opting For Outsourcing Data To Remote Cloud Service Providers (CSPs). Customers Can Rent The CSPs Storage Infrastructure To Store And Retrieve Almost Unlimited Amount Of Data By Paying Fees Metered In Gigabyte/month. For An Increased Level Of Scalability, Availability, And Durability, Some Customers May Want Their Data To Be Replicated On Multiple Servers Across Multiple Data Centers. The More Copies The CSP Is Asked To Store, The More Fees The Customers Are Charged. Therefore, Customers Need To Have A Strong Guarantee That The CSP Is Storing All Data Copies That Are Agreed Upon In The Service Contract, And All These Copies Are Consistent With The Most Recent Modifications Issued By The Customers. In This Paper, We Propose A Map-based Provable Multicopy Dynamic Data Possession (MB-PMDDP) Scheme That Has The Following Features: 1) It Provides An Evidence To The Customers That The CSP Is Not Cheating By Storing Fewer Copies; 2) It Supports Outsourcing Of Dynamic Data, I.e., It Supports Block-level Operations, Such As Block Modification, Insertion, Deletion, And Append; And 3) It Allows Authorized Users To Seamlessly Access The File Copies Stored By The CSP. We Give A Comparative Analysis Of The Proposed MB-PMDDP Scheme With A Reference Model Obtained By Extending Existing Provable Possession Of Dynamic Single-copy Schemes. The Theoretical Analysis Is Validated Through Experimental Results On A Commercial Cloud Platform. In Addition, We Show The Security Against Colluding Servers, And Discuss How To Identify Corrupted Copies By Slightly Modifying The Proposed Scheme.

SECURITY ANALYSIS ON CLOUD- Security Analysis On One-to-Many Order Preserving Encryption Based Cloud Data Search

For Ranked Search In Encrypted Cloud Data, Order Preserving Encryption (OPE) Is An Efficient Tool To Encrypt Relevance Scores Of The Inverted Index. When Using Deterministic OPE, The Ciphertexts Will Reveal The Distribution Of Relevance Scores. Therefore, Wang Et Al. Proposed A Probabilistic OPE, Called One-to-many OPE, For Applications Of Searchable Encryption, Which Can Flatten The Distribution Of The Plaintexts. In This Paper, We Proposed A Differential Attack On One-to-many OPE By Exploiting The Differences Of The Ordered Ciphertexts. The Experimental Results Show That The Cloud Server Can Get A Good Estimate Of The Distribution Of Relevance Scores By A Differential Attack. Furthermore, When Having Some Background Information On The Outsourced Documents, The Cloud Server Can Accurately Infer The Encrypted Keywords Using The Estimated Distributions.

RE-ENCRYPTION- Reliable Re-Encryption In Unreliable Clouds

A Key Approach To Secure Cloud Computing Is For The Data Owner To Store Encrypted Data In The Cloud, And Issue Decryption Keys To Authorized Users. Then, When A User Is Revoked, The Data Owner Will Issue Re-encryption Commands To The Cloud To Re-encrypt The Data, To Prevent The Revoked User From Decrypting The Data, And To Generate New Decryption Keys To Valid Users, So That They Can Continue To Access The Data. However, Since A Cloud Computing Environment Is Comprised Of Many Cloud Servers, Such Commands May Not Be Received And Executed By All Of The Cloud Servers Due To Unreliable Network Communications. In This Paper, We Solve This Problem By Proposing A Time-based Re-encryption Scheme, Which Enables The Cloud Servers To Automatically Re-encrypt Data Based On Their Internal Clocks. Our Solution Is Built On Top Of A New Encryption Scheme, Attribute-based Encryption, To Allow Fine-grain Access Control, And Does Not Require Perfect Clock Synchronization For Correctness.

PERSONAL HEALTH DATA- Lifelong Personal Health Data And Application Software Via Virtual Machines The Cloud

Personal Health Records (PHRs) Should Remain The Lifelong Property Of Patients, Who Should Be Able To Show Them Conveniently And Securely To Selected Caregivers And Institutions. In This Paper, We Present MyPHRMachines, A Cloud-based PHR System Taking A Radically New Architectural Solution To Health Record Portability. In MyPHRMachines, Health-related Data And The Application Software To View And/or Analyze It Are Separately Deployed In The PHR System. After Uploading Their Medical Data To MyPHRMachines, Patients Can Access Them Again From Remote Virtual Machines That Contain The Right Software To Visualize And Analyze Them Without Any Need For Conversion. Patients Can Share Their Remote Virtual Machine Session With Selected Caregivers, Who Will Need Only A Web Browser To Access The Pre-loaded Fragments Of Their Lifelong PHR. We Discuss A Prototype Of MyPHRMachines Applied To Two Use Cases, I.e., Radiology Image Sharing And Personalized Medicine.

DIFFERENTIAL QUERY SERVICES- Towards Differential Query Services In Cost-Efficient Clouds

Cloud Computing As An Emerging Technology Trend Is Expected To Reshape The Advances In Information Technology An Efficient Information Retrieval For Ranked Queries (EIRQ) Scheme Is Recovery Of Ranked Files On User Demand. An EIRQ Worked Based On The Aggregation And Distribution Layer (ADL). An ADL Is Act As Mediator Between Cloud And End-users. An EIRQ Scheme Reduces The Communication Cost And Communication Overhead. Mask Matrix Is Used To Filter Out As What User Really Wants Matched Data Before Recurring To The Aggregation And Distribution Layer (ADL). A User Can Retrieve Files On Demand By Choosing Queries Of Different Ranks. This Feature Is Useful When There Are A Large Number Of Matched Files, But The User Only Needs A Small Subset Of Them. Under Different Parameter Settings, Extensive Evaluations Have Been Conducted On Both Analytical Models And On A Real Cloud Environment, In Order To Examine The Effectiveness Of Our Schemes To Avoid Small Scale Of Interruptions In Cloud Computing, Follow Two Essential Issues:-Privacy And Efficiency. Private Keyword Based File Retrieval Scheme Was Anticipated By Ostrovsky.

DATA SHARING IN PUBLIC CLOUDS- An Efficient Certificateless Encryption For Secure Data Sharing In Public Clouds

We Propose A Mediated Certificateless Encryption Scheme Without Pairing Operations For Securely Sharing Sensitive Information In Public Clouds. Mediated Certificateless Public Key Encryption (mCL-PKE) Solves The Key Escrow Problem In Identity Based Encryption And Certificate Revocation Problem In Public Key Cryptography. However, Existing MCL-PKE Schemes Are Either Inefficient Because Of The Use Of Expensive Pairing Operations Or Vulnerable Against Partial Decryption Attacks. In Order To Address The Performance And Security Issues, In This Paper, We First Propose A MCL-PKE Scheme Without Using Pairing Operations. We Apply Our MCL-PKE Scheme To Construct A Practical Solution To The Problem Of Sharing Sensitive Information In Public Clouds. The Cloud Is Employed As A Secure Storage As Well As A Key Generation Center. In Our System, The Data Owner Encrypts The Sensitive Data Using The Cloud Generated Users' Public Keys Based On Its Access Control Policies And Uploads The Encrypted Data To The Cloud. Upon Successful Authorization, The Cloud Partially Decrypts The Encrypted Data For The Users. The Users Subsequently Fully Decrypt The Partially Decrypted Data Using Their Private Keys. The Confidentiality Of The Content And The Keys Is Preserved With Respect To The Cloud, Because The Cloud Cannot Fully Decrypt The Information. We Also Propose An Extension To The Above Approach To Improve The Efficiency Of Encryption At The Data Owner. We Implement Our MCL-PKE Scheme And The Overall Cloud Based System, And Evaluate Its Security And Performance. Our Results Show That Our Schemes Are Efficient And Practical.

TRUST MANAGEMENT FOR CLOUD SERVICES Cloud Armor Supporting Reputation-based Trust Management For Cloud Services

Trust Management Is One Of The Most Challenging Issues For The Adoption And Growth Of Cloud Computing. The Highly Dynamic, Distributed, And Non-transparent Nature Of Cloud Services Introduces Several Challenging Issues Such As Privacy, Security, And Availability. Preserving Consumers' Privacy Is Not An Easy Task Due To The Sensitive Information Involved In The Interactions Between Consumers And The Trust Management Service. Protecting Cloud Services Against Their Malicious Users (e.g., Such Users Might Give Misleading Feedback To Disadvantage A Particular Cloud Service) Is A Difficult Problem. Guaranteeing The Availability Of The Trust Management Service Is Another Significant Challenge Because Of The Dynamic Nature Of Cloud Environments. In This Article, We Describe The Design And Implementation Of CloudArmor, A Reputation-based Trust Management Framework That Provides A Set Of Functionalities To Deliver Trust As A Service (TaaS), Which Includes I) A Novel Protocol To Prove The Credibility Of Trust Feedbacks And Preserve Users' Privacy, Ii) An Adaptive And Robust Credibility Model For Measuring The Credibility Of Trust Feedbacks To Protect Cloud Services From Malicious Users And To Compare The Trustworthiness Of Cloud Services, And Iii) An Availability Model To Manage The Availability Of The Decentralized Implementation Of The Trust Management Service. The Feasibility And Benefits Of Our Approach Have Been Validated By A Prototype And Experimental Studies Using A Collection Of Real-world Trust Feedbacks On Cloud Services.

OPTIMAL PERFORMANCE AND SECURITY- DROPS Division And Replication Of Data In Cloud For Optimal Performance And Security

Outsourcing Data To A Third-party Administrative Control, As Is Done In Cloud Computing, Gives Rise To Security Concerns. The Data Compromise May Occur Due To Attacks By Other Users And Nodes Within The Cloud. Therefore, High Security Measures Are Required To Protect Data Within The Cloud. However, The Employed Security Strategy Must Also Take Into Account The Optimization Of The Data Retrieval Time. In This Paper, We Propose Division And Replication Of Data In The Cloud For Optimal Performance And Security (DROPS) That Collectively Approaches The Security And Performance Issues. In The DROPS Methodology, We Divide A File Into Fragments, And Replicate The Fragmented Data Over The Cloud Nodes. Each Of The Nodes Stores Only A Single Fragment Of A Particular Data File That Ensures That Even In Case Of A Successful Attack, No Meaningful Information Is Revealed To The Attacker. Moreover, The Nodes Storing The Fragments, Are Separated With Certain Distance By Means Of Graph T-coloring To Prohibit An Attacker Of Guessing The Locations Of The Fragments. Furthermore, The DROPS Methodology Does Not Rely On The Traditional Cryptographic Techniques For The Data Security; Thereby Relieving The System Of Computationally Expensive Methodologies. We Show That The Probability To Locate And Compromise All Of The Nodes Storing The Fragments Of A Single File Is Extremely Low. We Also Compare The Performance Of The DROPS Methodology With 10 Other Schemes. The Higher Level Of Security With Slight Performance Overhead Was Observed.

SERVICE ORIENTED MOBILE SOCIAL NETWORK- Enabling Trustworthy Service Evaluation In Service-Oriented Mobile Social Networks

In This Paper, We Propose A Trustworthy Service Evaluation (TSE) System To Enable Users To Share Service Reviews In Service-oriented Mobile Social Networks (S-MSNs). Each Service Provider Independently Maintains A TSE For Itself, Which Collects And Stores Users' Reviews About Its Services Without Requiring Any Third Trusted Authority. The Service Reviews Can Then Be Made Available To Interested Users In Making Wise Service Selection Decisions. We Identify Three Unique Service Review Attacks, I.e., Linkability, Rejection, And Modification Attacks, And Develop Sophisticated Security Mechanisms For The TSE To Deal With These Attacks. Specifically, The Basic TSE (bTSE) Enables Users To Distributedly And Cooperatively Submit Their Reviews In An Integrated Chain Form By Using Hierarchical And Aggregate Signature Techniques. It Restricts The Service Providers To Reject, Modify, Or Delete The Reviews. Thus, The Integrity And Authenticity Of Reviews Are Improved. Further, We Extend The BTSE To A Sybil-resisted TSE (SrTSE) To Enable The Detection Of Two Typical Sybil Attacks. In The SrTSE, If A User Generates Multiple Reviews Toward A Vendor In A Predefined Time Slot With Different Pseudonyms, The Real Identity Of That User Will Be Revealed. Through Security Analysis And Numerical Results, We Show That The BTSE And The SrTSE Effectively Resist The Service Review Attacks And The SrTSE Additionally Detects The Sybil Attacks In An Efficient Manner. Through Performance Evaluation, We Show That The BTSE Achieves Better Performance In Terms Of Submission Rate And Delay Than A Service Review System That Does Not Adopt User Cooperation.

RECOMMEND FOR USER INTEREST AND SOCIAL CIRCLE - Personalized Recommendation Combining User Interest And Social Circle

With The Advent And Popularity Of Social Network, More And More Users Like To Share Their Experiences, Such As Ratings, Reviews, And Blogs. The New Factors Of Social Network Like Interpersonal Influence And Interest Based On Circles Of Friends Bring Opportunities And Challenges For Recommender System To Solve The Cold Start And Sparsity Problem Of Datasets. Some Of The Social Factors Have Been Used In RS, But Have Not Been Fully Considered. In This Paper, Three Social Factors, Personal Interest, Interpersonal Interest Similarity, And Interpersonal Influence, Fuse Into A Unified Personalized Recommendation Model Based On Probabilistic Matrix Factorization. The Factor Of Personal Interest Can Make The RS Recommend Items To Meet Users' Individualities, Especially For Experienced Users. Moreover, For Cold Start Users, The Interpersonal Interest Similarity And Interpersonal Influence Can Enhance The Intrinsic Link Among Features In The Latent Space. We Conduct A Series Of Experiments On Three Rating Datasets: Yelp, MovieLens, And Douban Movie. Experimental Results Show The Proposed Approach Outperforms The Existing RS Approaches.

DATA SHARING IN CLOUDS- SeDaSC Secure Data Sharing In Clouds

Cloud Storage Is An Application Of Clouds That Liberates Organizations From Establishing In-house Data Storage Systems. However, Cloud Storage Gives Rise To Security Concerns. In Case Of Group-shared Data, The Data Face Both Cloud-specific And Conventional Insider Threats. Secure Data Sharing Among A Group That Counters Insider Threats Of Legitimate Yet Malicious Users Is An Important Research Issue. In This Paper, We Propose The Secure Data Sharing In Clouds (SeDaSC) Methodology That Provides: 1) Data Confidentiality And Integrity; 2) Access Control; 3) Data Sharing (forwarding) Without Using Compute-intensive Reencryption; 4) Insider Threat Security; And 5) Forward And Backward Access Control. The SeDaSC Methodology Encrypts A File With A Single Encryption Key. Two Different Key Shares For Each Of The Users Are Generated, With The User Only Getting One Share. The Possession Of A Single Share Of A Key Allows The SeDaSC Methodology To Counter The Insider Threats. The Other Key Share Is Stored By A Trusted Third Party, Which Is Called The Cryptographic Server. The SeDaSC Methodology Is Applicable To Conventional And Mobile Cloud Computing Environments. We Implement A Working Prototype Of The SeDaSC Methodology And Evaluate Its Performance Based On The Time Consumed During Various Operations. We Formally Verify The Working Of SeDaSC By Using High-level Petri Nets, The Satisfiability Modulo Theories Library, And A Z3 Solver. The Results Proved To Be Encouraging And Show That SeDaSC Has The Potential To Be Effectively Used For Secure Data Sharing In The Cloud.

TRUST-BUT-VERIFY- Verifying Result Correctness Of Outsourced Frequent Item Set Mining In Data-mining-as-a-service Paradigm

Cloud Computing Is Popularizing The Computing Paradigm In Which Data Is Outsourced To A Third-party Service Provider (server) For Data Mining. Outsourcing, However, Raises A Serious Security Issue: How Can The Client Of Weak Computational Power Verify That The Server Returned Correct Mining Result? In This Paper, We Focus On The Specific Task Of Frequent Itemset Mining. We Consider The Server That Is Potentially Untrusted And Tries To Escape From Verification By Using Its Prior Knowledge Of The Outsourced Data. We Propose Efficient Probabilistic And Deterministic Verification Approaches To Check Whether The Server Has Returned Correct And Complete Frequent Itemsets. Our Probabilistic Approach Can Catch Incorrect Results With High Probability, While Our Deterministic Approach Measures The Result Correctness With 100 Percent Certainty. We Also Design Efficient Verification Methods For Both Cases That The Data And The Mining Setup Are Updated. We Demonstrate The Effectiveness And Efficiency Of Our Methods Using An Extensive Set Of Empirical Results On Real Datasets.

DATA SECURITY IN CLOUD COMPUTING- Data Security In Cloud Computing Using Blowfish Algorithm

Cloud Computing Has Great Potential Of Providing Robust Computational Power To The Society At Reduced Cost. It Enables Customers With Limited Computational Resources To Outsource Their Large Computation Workloads To The Cloud, And Economically Enjoy The Massive Computational Power, Bandwidth, Storage, And Even Appropriate Software That Can Be Shared In A Pay-per-use Manner .Storing Data In A Third Party’s Cloud System Causes Serious Concern Over Data Confidentiality. General Encryption Schemes Protect Data Confidentiality, But Also Limit The Functionality Of The Storage System Because A Few Operations Are Supported Over Encrypted Data. Constructing A Secure Storage System That Supports Multiple Functions Is Challenging When The Storage System Is Distributed And Has No Central Authority. We Propose A Threshold Proxy Re-encryption Scheme And Integrate It With A Decentralized Erasure Code Such That A Secure Distributed Storage System Is Formulated. The Distributed Storage System Not Only Supports Secure And Robust Data Storage And Retrieval, But Also Lets A User Forward His Data In The Storage Servers To Another User Without Retrieving The Data Back. The Main Technical Contribution Is That The Proxy Re-encryption Scheme Supports Encoding Operations Over Encrypted Messages As Well As Forwarding Operations Over Encoded And Encrypted Messages. Our Method Fully Integrates Encrypting, Encoding, And Forwarding. We Analyze And Suggest Suitable Parameters For The Number Of Copies Of A Message Dispatched To Storage Servers And The Number Of Storage Servers Queried By A Key Server.

SERVICE MANAGEMENT IN CLOUD- A Flexible Architecture For Service Management In The Cloud

Cloud Computing Is A Style Of Computing Where Different Capabilities Are Provided As A Service To Customers Using Internet Technologies. The Most Common Offered Services Are Infrastructure (IasS), Software (SaaS) And Platform (PaaS). This Work Integrates The Service Management Into The Cloud Computing Concept And Shows How Management Can Be Provided As A Service In The Cloud. Nowadays, Services Need To Adapt Their Functionalities Across Heterogeneous Environments With Different Technological And Administrative Domains. The Implied Complexity Of This Situation Can Be Simplified By A Service Management Architecture In The Cloud. This Paper Focuses On This Architecture, Taking Into Account Specific Service Management Functionalities, Like Incident Management Or KPI/SLA Management, And Provides A Complete Solution. The Proposed Architecture Is Based On A Distributed Set Of Agents, Using Semantic-based Techniques: A Shared Knowledge Plane, Instantiated In The Cloud, Has Been Introduced To Ensure Communication Between Agents.

CLOUD PROVIDERS- A Novel Economic Sharing Model In A Federation Of Selfish Cloud Providers

This Paper Presents A Novel Economic Model To Regulate Capacity Sharing In A Federation Of Hybrid Cloud Providers (CPs). The Proposed Work Models The Interactions Among The CPs As A Repeated Game Among Selfish Players That Aim At Maximizing Their Profit By Selling Their Unused Capacity In The Spot Market But Are Uncertain Of Future Workload Fluctuations. The Proposed Work First Establishes That The Uncertainty In Future Revenue Can Act As A Participation Incentive To Sharing In The Repeated Game. We, Then, Demonstrate How An Efficient Sharing Strategy Can Be Obtained Via Solving A Simple Dynamic Programming Problem. The Obtained Strategy Is A Simple Update Rule That Depends Only On The Current Workloads And A Single Variable Summarizing Past Interactions. In Contrast To Existing Approaches, The Model Incorporates Historical And Expected Future Revenue As Part Of The Virtual Machine (VM) Sharing Decision. Moreover, These Decisions Are Not Enforced Neither By A Centralized Broker Nor By Predefined Agreements. Rather, The Proposed Model Employs A Simple Grim Trigger Strategy Where A CP Is Threatened By The Elimination Of Future VM Hosting By Other CPs. Simulation Results Demonstrate The Performance Of The Proposed Model In Terms Of The Increased Profit And The Reduction In The Variance In The Spot Market VM Availability And Prices.

A SOCIAL COMPUTE CLOUD- Allocating And Sharing Infrastructure Resources Via Social Networks

Social Network Platforms Have Rapidly Changed The Way That People Communicate And Interact. They Have Enabled The Establishment Of, And Participation In, Digital Communities As Well As The Representation, Documentation And Exploration Of Social Relationships. We Believe That As `apps' Become More Sophisticated, It Will Become Easier For Users To Share Their Own Services, Resources And Data Via Social Networks. To Substantiate This, We Present A Social Compute Cloud Where The Provisioning Of Cloud Infrastructure Occurs Through “friend” Relationships. In A Social Compute Cloud, Resource Owners Offer Virtualized Containers On Their Personal Computer(s) Or Smart Device(s) To Their Social Network. However, As Users May Have Complex Preference Structures Concerning With Whom They Do Or Do Not Wish To Share Their Resources, We Investigate, Via Simulation, How Resources Can Be Effectively Allocated Within A Social Community Offering Resources On A Best Effort Basis. In The Assessment Of Social Resource Allocation, We Consider Welfare, Allocation Fairness, And Algorithmic Runtime. The Key Findings Of This Work Illustrate How Social Networks Can Be Leveraged In The Construction Of Cloud Computing Infrastructures And How Resources Can Be Allocated In The Presence Of User Sharing Preferences.

INVESTIGATE DATA CENTER PERFORMANCE- A Stochastic Model To Investigate Data Center Performance And QoS In IaaS Cloud Computing Systems

Cloud Data Center Management Is A Key Problem Due To The Numerous And Heterogeneous Strategies That Can Be Applied, Ranging From The VM Placement To The Federation With Other Clouds. Performance Evaluation Of Cloud Computing Infrastructures Is Required To Predict And Quantify The Cost-benefit Of A Strategy Portfolio And The Corresponding Quality Of Service (QoS) Experienced By Users. Such Analyses Are Not Feasible By Simulation Or On-the-field Experimentation, Due To The Great Number Of Parameters That Have To Be Investigated. In This Paper, We Present An Analytical Model, Based On Stochastic Reward Nets (SRNs), That Is Both Scalable To Model Systems Composed Of Thousands Of Resources And Flexible To Represent Different Policies And Cloud-specific Strategies. Several Performance Metrics Are Defined And Evaluated To Analyze The Behavior Of A Cloud Data Center: Utilization, Availability, Waiting Time, And Responsiveness. A Resiliency Analysis Is Also Provided To Take Into Account Load Bursts. Finally, A General Approach Is Presented That, Starting From The Concept Of System Capacity, Can Help System Managers To Opportunely Set The Data Center Parameters Under Different Working Conditions.

PRIVACY PRESERVING AUTHENTICATION - Shared Authority Based Privacy-preserving Authentication Protocol In Cloud Computing

Cloud Computing Is An Emerging Data Interactive Paradigm To Realize Users' Data Remotely Stored In An Online Cloud Server. Cloud Services Provide Great Conveniences For The Users To Enjoy The On-demand Cloud Applications Without Considering The Local Infrastructure Limitations. During The Data Accessing, Different Users May Be In A Collaborative Relationship, And Thus Data Sharing Becomes Significant To Achieve Productive Benefits. The Existing Security Solutions Mainly Focus On The Authentication To Realize That A User's Privative Data Cannot Be Illegally Accessed, But Neglect A Subtle Privacy Issue During A User Challenging The Cloud Server To Request Other Users For Data Sharing. The Challenged Access Request Itself May Reveal The User's Privacy No Matter Whether Or Not It Can Obtain The Data Access Permissions. In This Paper, We Propose A Shared Authority Based Privacy-preserving Authentication Protocol (SAPA) To Address Above Privacy Issue For Cloud Storage. In The SAPA, 1) Shared Access Authority Is Achieved By Anonymous Access Request Matching Mechanism With Security And Privacy Considerations (e.g., Authentication, Data Anonymity, User Privacy, And Forward Security); 2) Attribute Based Access Control Is Adopted To Realize That The User Can Only Access Its Own Data Fields; 3) Proxy Re-encryption Is Applied To Provide Data Sharing Among The Multiple Users. Meanwhile, Universal Composability (UC) Model Is Established To Prove That The SAPA Theoretically Has The Design Correctness. It Indicates That The Proposed Protocol Is Attractive For Multi-user Collaborative Cloud Applications.

SECURE AUTHERIZED DEDUPLICATION - A Hybrid Cloud Approach For Secure Authorized Deduplication

Data Deduplication Is One Of Important Data Compression Techniques For Eliminating Duplicate Copies Of Repeating Data, And Has Been Widely Used In Cloud Storage To Reduce The Amount Of Storage Space And Save Bandwidth. To Protect The Confidentiality Of Sensitive Data While Supporting Deduplication, The Convergent Encryption Technique Has Been Proposed To Encrypt The Data Before Outsourcing. To Better Protect Data Security, This Paper Makes The First Attempt To Formally Address The Problem Of Authorized Data Deduplication. Different From Traditional Deduplication Systems, The Differential Privileges Of Users Are Further Considered In Duplicate Check Besides The Data Itself. We Also Present Several New Deduplication Constructions Supporting Authorized Duplicate Check In A Hybrid Cloud Architecture. Security Analysis Demonstrates That Our Scheme Is Secure In Terms Of The Definitions Specified In The Proposed Security Model. As A Proof Of Concept, We Implement A Prototype Of Our Proposed Authorized Duplicate Check Scheme And Conduct Testbed Experiments Using Our Prototype. We Show That Our Proposed Authorized Duplicate Check Scheme Incurs Minimal Overhead Compared To Normal Operations.

AUDITING FOR SHARED DATAIN CLOUD Privacy-Preserving Public Auditing For Shared Data In The Cloud

With Cloud Data Services, It Is Commonplace For Data To Be Not Only Stored In The Cloud, But Also Shared Across Multiple Users. Unfortunately, The Integrity Of Cloud Data Is Subject To Skepticism Due To The Existence Of Hardware/software Failures And Human Errors. Several Mechanisms Have Been Designed To Allow Both Data Owners And Public Verifiers To Efficiently Audit Cloud Data Integrity Without Retrieving The Entire Data From The Cloud Server. However, Public Auditing On The Integrity Of Shared Data With These Existing Mechanisms Will Inevitably Reveal Confidential Information—identity Privacy—to Public Verifiers. In This Paper, We Propose A Novel Privacy-preserving Mechanism That Supports Public Auditing On Shared Data Stored In The Cloud. In Particular, We Exploit Ring Signatures To Compute Verification Metadata Needed To Audit The Correctness Of Shared Data. With Our Mechanism, The Identity Of The Signer On Each Block In Shared Data Is Kept Private From Public Verifiers, Who Are Able To Efficiently Verify Shared Data Integrity Without Retrieving The Entire File. In Addition, Our Mechanism Is Able To Perform Multiple Auditing Tasks Simultaneously Instead Of Verifying Them One By One. Our Experimental Results Demonstrate The Effectiveness And Efficiency Of Our Mechanism When Auditing Shared Data Integrity.

PERFORMACE AND COST EVALUATION - Performance And Cost Evaluation Of An Adaptive Encryption Architecture For Cloud Databases

The Cloud Database As A Service Is A Novel Paradigm That Can Support Several Internet-based Applications, But Its Adoption Requires The Solution Of Information Confidentiality Problems. We Propose A Novel Architecture For Adaptive Encryption Of Public Cloud Databases That Offers An Interesting Alternative To The Tradeoff Between The Required Data Confidentiality Level And The Flexibility Of The Cloud Database Structures At Design Time. We Demonstrate The Feasibility And Performance Of The Proposed Solution Through A Software Prototype. Moreover, We Propose An Original Cost Model That Is Oriented To The Evaluation Of Cloud Database Services In Plain And Encrypted Instances And That Takes Into Account The Variability Of Cloud Prices And Tenant Workloads During A Medium-term Period.

SOCIAL VIDEO SHARING IN CLOUD - AMES-Cloud Framework Of Adaptive Mobile Video Streaming And Efficient Social Video Sharing In The Clouds

While Demands On Video Traffic Over Mobile Networks Have Been Souring, The Wireless Link Capacity Cannot Keep Up With The Traffic Demand. The Gap Between The Traffic Demand And The Link Capacity, Along With Time-varying Link Conditions, Results In Poor Service Quality Of Video Streaming Over Mobile Networks Such As Long Buffering Time And Intermittent Disruptions. Leveraging The Cloud Computing Technology, We Propose A New Mobile Video Streaming Framework, Dubbed AMES-Cloud, Which Has Two Main Parts: Adaptive Mobile Video Streaming (AMoV) And Efficient Social Video Sharing (ESoV). AMoV And ESoV Construct A Private Agent To Provide Video Streaming Services Efficiently For Each Mobile User. For A Given User, AMoV Lets Her Private Agent Adaptively Adjust Her Streaming Flow With A Scalable Video Coding Technique Based On The Feedback Of Link Quality. Likewise, ESoV Monitors The Social Network Interactions Among Mobile Users, And Their Private Agents Try To Prefetch Video Content In Advance. We Implement A Prototype Of The AMES-Cloud Framework To Demonstrate Its Performance. It Is Shown That The Private Agents In The Clouds Can Effectively Provide The Adaptive Streaming, And Perform Video Sharing (i.e., Prefetching) Based On The Social Network Analysis.

DATA SHARING FOR DYNAMIC GROPUS IN CLOUD - MONA-Multi-Owner Data Sharing For Dynamic Groups In The Cloud

With The Character Of Low Maintenance, Cloud Computing Provides An Economical And Efficient Solution For Sharing Group Resource Among Cloud Users. Unfortunately, Sharing Data In A Multi-owner Manner While Preserving Data And Identity Privacy From An Untrusted Cloud Is Still A Challenging Issue, Due To The Frequent Change Of The Membership. In This Paper, We Propose A Secure Multi-owner Data Sharing Scheme, Named Mona, For Dynamic Groups In The Cloud. By Leveraging Group Signature And Dynamic Broadcast Encryption Techniques, Any Cloud User Can Anonymously Share Data With Others. Meanwhile, The Storage Overhead And Encryption Computation Cost Of Our Scheme Are Independent With The Number Of Revoked Users. In Addition, We Analyze The Security Of Our Scheme With Rigorous Proofs, And Demonstrate The Efficiency Of Our Scheme In Experiments

DATA SECURITY IN CLOUD COMPUTING - Ensuring Data Security In Cloud Computing

With The Advent Of Cloud Computing, Data Owners Are Motivated To Outsource Their Complex Data Management Systems From Local Sites To Commercial Public Cloud For Great Flexibility And Economic Savings. But For Protecting Data Privacy, Sensitive Data Has To Be Encrypted Before Outsourcing, Which Obsoletes Traditional Data Utilization Based On Plaintext Keyword Search. Thus, Enabling An Encrypted Cloud Data Search Service Is Of Paramount Importance. Considering The Large Number Of Data Users And Documents In Cloud, It Is Crucial For The Search Service To Allow Multi-keyword Query And Provide Result Similarity Ranking To Meet The Effective Data Retrieval Need. Related Works On Searchable Encryption Focus On Single Keyword Search Or Boolean Keyword Search, And Rarely Differentiate The Search Results. In This Paper, For The First Time, We Define And Solve The Challenging Problem Of Privacy-preserving Multi-keyword Ranked Search Over Encrypted Cloud Data (MRSE), And Establish A Set Of Strict Privacy Requirements For Such A Secure Cloud Data Utilization System To Become A Reality. Among Various Multi-keyword Semantics, We Choose The Efficient Principle Of “coordinate Matching”, I.e., As Many Matches As Possible, To Capture The Similarity Between Search Query And Data Documents, And Further Use “inner Product Similarity” To Quantitatively Formalize Such Principle For Similarity Measurement. We First Propose A Basic MRSE Scheme Using Secure Inner Product Computation, And Then Significantly Improve It To Meet Different Privacy Requirements In Two Levels Of Threat Models. Thorough Analysis Investigating Privacy And Efficiency Guarantees Of Proposed Schemes Is Given, And Experiments On The Real-world Dataset Further Show Proposed Schemes Indeed Introduce Low Overhead On Computation And Communication.

MULTI CLOUD ARCHITECTURE - Security And Privacy Enhancing Multi-Cloud Architecture

Security Challenges Are Still Among The Biggest Obstacles When Considering The Adoption Of Cloud Services. This Triggered A Lot Of Research Activities, Resulting In A Quantity Of Proposals Targeting The Various Cloud Security Threats. Alongside With These Security Issues, The Cloud Paradigm Comes With A New Set Of Unique Features, Which Open The Path Toward Novel Security Approaches, Techniques, And Architectures. This Paper Provides A Survey On The Achievable Security Merits By Making Use Of Multiple Distinct Clouds Simultaneously. Various Distinct Architectures Are Introduced And Discussed According To Their Security And Privacy Capabilities And Prospects.

SERVICE BASED APPLICATION IN THE CLOUD- A Decentralized Self-adaptation Mechanism For Service-based Applications In The Cloud

Cloud Computing, With Its Promise Of (almost) Unlimited Computation, Storage, And Bandwidth, Is Increasingly Becoming The Infrastructure Of Choice For Many Organizations. As Cloud Offerings Mature, Service-based Applications Need To Dynamically Recompose Themselves To Self-adapt To Changing QoS Requirements. In This Paper, We Present A Decentralized Mechanism For Such Self-adaptation, Using Market-based Heuristics. We Use A Continuous Double-auction To Allow Applications To Decide Which Services To Choose, Among The Many On Offer. We View An Application As A Multi-agent System And The Cloud As A Marketplace Where Many Such Applications Self-adapt. We Show Through A Simulation Study That Our Mechanism Is Effective For The Individual Application As Well As From The Collective Perspective Of All Applications Adapting At The Same Time.

SECURE DATA FORWARDING - A Secure Erasure Code Based Cloud Storage System With Secure Data Forwarding

A Cloud Storage System, Consisting Of A Collection Of Storage Servers, Provides Long-term Storage Services Over The Internet. Storing Data In A Third Party's Cloud System Causes Serious Concern Over Data Confidentiality. General Encryption Schemes Protect Data Confidentiality, But Also Limit The Functionality Of The Storage System Because A Few Operations Are Supported Over Encrypted Data. Constructing A Secure Storage System That Supports Multiple Functions Is Challenging When The Storage System Is Distributed And Has No Central Authority. We Propose A Threshold Proxy Re-encryption Scheme And Integrate It With A Decentralized Erasure Code Such That A Secure Distributed Storage System Is Formulated. The Distributed Storage System Not Only Supports Secure And Robust Data Storage And Retrieval, But Also Lets A User Forward His Data In The Storage Servers To Another User Without Retrieving The Data Back. The Main Technical Contribution Is That The Proxy Re-encryption Scheme Supports Encoding Operations Over Encrypted Messages As Well As Forwarding Operations Over Encoded And Encrypted Messages. Our Method Fully Integrates Encrypting, Encoding, And Forwarding. We Analyze And Suggest Suitable Parameters For The Number Of Copies Of A Message Dispatched To Storage Servers And The Number Of Storage Servers Queried By A Key Server. These Parameters Allow More Flexible Adjustment Between The Number Of Storage Servers And Robustness.

HIERARCHICAL ATTRIBUTE - HASBE-A Hierarchical Attribute Based Solution For Flexible And Scalable Access Control In Cloud Computing

Cloud Computing Has Emerged As One Of The Most Influential Paradigms In The IT Industry In Recent Years. Since This New Computing Technology Requires Users To Entrust Their Valuable Data To Cloud Providers, There Have Been Increasing Security And Privacy Concerns On Outsourced Data. Several Schemes Employing Attribute-based Encryption (ABE) Have Been Proposed For Access Control Of Outsourced Data In Cloud Computing; However, Most Of Them Suffer From Inflexibility In Implementing Complex Access Control Policies. In Order To Realize Scalable, Flexible, And Fine-grained Access Control Of Outsourced Data In Cloud Computing, In This Paper, We Propose Hierarchical Attribute-set-based Encryption (HASBE) By Extending Ciphertext-policy Attribute-set-based Encryption (ASBE) With A Hierarchical Structure Of Users. The Proposed Scheme Not Only Achieves Scalability Due To Its Hierarchical Structure, But Also Inherits Flexibility And Fine-grained Access Control In Supporting Compound Attributes Of ASBE. In Addition, HASBE Employs Multiple Value Assignments For Access Expiration Time To Deal With User Revocation More Efficiently Than Existing Schemes. We Formally Prove The Security Of HASBE Based On Security Of The Ciphertext-policy Attribute-based Encryption Scheme By Bethencourt And Analyze Its Performance And Computational Complexity. We Implement Our Scheme And Show That It Is Both Efficient And Flexible In Dealing With Access Control For Outsourced Data In Cloud Computing With Comprehensive Experiments.

RANKING PREDICTION - QoS Ranking Prediction For Cloud Services

Cloud Computing Is Becoming Popular. Building High-quality Cloud Applications Is A Critical Research Problem. QoS Rankings Provide Valuable Information For Making Optimal Cloud Service Selection From A Set Of Functionally Equivalent Service Candidates. To Obtain QoS Values, Real-world Invocations On The Service Candidates Are Usually Required. To Avoid The Time-consuming And Expensive Real-world Service Invocations, This Paper Proposes A QoS Ranking Prediction Framework For Cloud Services By Taking Advantage Of The Past Service Usage Experiences Of Other Consumers. Our Proposed Framework Requires No Additional Invocations Of Cloud Services When Making QoS Ranking Prediction. Two Personalized QoS Ranking Prediction Approaches Are Proposed To Predict The QoS Rankings Directly. Comprehensive Experiments Are Conducted Employing Real-world QoS Data, Including 300 Distributed Users And 500 Real-world Web Services All Over The World. The Experimental Results Show That Our Approaches Outperform Other Competing Approaches.

CQA POST VOTING PREDICTION - QAAN Question Answering Attention Networking For Community Question Classification

Community Question Answering (CQA) Provides Platforms For Users With Various Backgrounds To Obtain Information And Share Knowledge. In Recent Years, With The Rapid Development Of Such Online Platforms, An Enormous Amount Of Archive Data Has Accumulated, It Becomes More And More Difficult For Expert Users To Identify Desirable Questions. In Order To Reduce The Proportion Of Unanswered Questions In CQA, Facilitate Expert Users To Find The Questions They Are Interested In, Question Classification Becomes An Important Task Of CQA, Which Aims To Assign A Newly Posted Question To A Specific Preset Category. In This Paper, We Propose A Novel Question Answering Attention Network (QAAN) For Investigating The Role Of The Paired Answer Of Questions For Classification. Specifically, QAAN Studies The Correlation Between Question And Paired Answer, Taking The Questions As The Primary Part Of The Question Representation, And The Answer Information Is Aggregated Based On Similarity And Disparity With The Answer. Our Experiment Is Implemented On Yahoo! Answers Dataset. The Results Show That QAAN Outperforms All The Baseline Models.

REPRESENTATIVE TRAVEL ROUTE RECOMMENDATION- Personalized Tourism Route Recommendation System Based On Dynamic Clustering Of User Groups

Tourism Path Dynamic Planning Is An Asynchronous Group Model Planning Problem. It Is Required To Find Group Patterns With Similar Trajectory Behavior Under The Constraint Of Unequal Time Intervals. Traditional Trajectory Group Pattern Mining Algorithms Often Deal With GPS Data With Fixed Time Interval Sampling Constraints, So They Can Not Be Directly Used In Coterie Pattern Mining. At The Same Time, Traditional Group Pattern Mining Has The Problem Of Lack Of Semantic Information, Which Reduces The Integrity And Accuracy Of Personalized Travel Route Recommendation. Therefore, This Paper Proposes A Semantic Based Distance Sensitive Recommendation Strategy. In Order To Efficiently Process Large-scale Social Network Trajectory Data, This Paper Uses MapReduce Programming Model With Optimized Clustering To Mine Coterie Group Patterns. The Experimental Results Show That: Under MapReduce Programming Model, Coterie Group Pattern Mining With Optimized Clustering And Semantic Information Is Superior To Traditional Group Mode In Personalized Travel Route Recommendation Quality, And Can Effectively Process Large-scale Social Network Trajectory Data.

CREDIT CARD FRAUD DETECTION - Fraud Detection In Credit Card Data Using Unsupervised Machine Learning Based Scheme

Development Of Communication Technologies And E-commerce Has Made The Credit Card As The Most Common Technique Of Payment For Both Online And Regular Purchases. So, Security In This System Is Highly Expected To Prevent Fraud Transactions. Fraud Transactions In Credit Card Data Transaction Are Increasing Each Year. In This Direction, Researchers Are Also Trying The Novel Techniques To Detect And Prevent Such Frauds. However, There Is Always A Need Of Some Techniques That Should Precisely And Efficiently Detect These Frauds. This Paper Proposes A Scheme For Detecting Frauds In Credit Card Data Which Uses A Neural Network (NN) Based Unsupervised Learning Technique. Proposed Method Outperforms The Existing Approaches Of Auto Encoder (AE), Local Outlier Factor (LOF), Isolation Forest (IF) And K-Means Clustering. Proposed NN Based Fraud Detection Method Performs With 99.87% Accuracy Whereas Existing Methods AE, IF, LOF And K Means Gives 97%, 98%, 98% And 99.75% Accuracy Respectively.

SECURE MINING OF ASSOCIATION RULES - Scalable Privacy-Preserving Distributed Extremely Randomized Trees For Structured Data With Multiple Colluding Parties

Today, In Many Real-world Applications Of Machine Learning Algorithms, The Data Is Stored On Multiple Sources Instead Of At One Central Repository. In Many Such Scenarios, Due To Privacy Concerns And Legal Obligations, E.g., For Medical Data, And Communication/computation Overhead, For Instance For Large Scale Data, The Raw Data Cannot Be Transferred To A Center For Analysis. Therefore, New Machine Learning Approaches Are Proposed For Learning From The Distributed Data In Such Settings. In This Paper, We Extend The Distributed Extremely Randomized Trees (ERT) Approach W.r.t. Privacy And Scalability. First, We Extend Distributed ERT To Be Resilient W.r.t. The Number Of Colluding Parties In A Scalable Fashion. Then, We Extend The Distributed ERT To Improve Its Scalability Without Any Major Loss In Classification Performance. We Refer To Our Proposed Approach As K-PPD-ERT Or Privacy-Preserving Distributed Extremely Randomized Trees With K Colluding Parties.

TAXI DRIVERS ROUTE CHOICE BEHAVIOR USING THE TRACE RECORDS- A Mixed Path Size Logit-Based Taxi Customer-Search Model Considering Spatio-Temporal Factors In Route Choice

This Paper Introduces A Model To Analyze Route Choice Behavior Of Taxi Drivers For Finding Next Passenger In Urban Road Network. Considering The Situation Of Path Overlapping Between Selected Routes In The Process Of Customer-searching, A Mixed Path Size Logit Model Is Proposed To Analyze Route Choice Behaviors Through Considering Spatio-temporal Features Of Route Including Customer Generation Rate, Path Travel Time, Cumulative Intersection Delay, Path Distance, And Path Size. Specially, Customer Generation Rate Is Defined As Attraction Strength Based On Historical Pick-up Records In The Route, The Intersection Travel Delay And Path Travel Time Are Estimated Based On Large Scaled Taxi Global Positioning System Trajectories. In The Experiment, The GPS Data Were Collected From About 36000 Taxi Vehicles In Beijing At 30-s Interval During Six Months. In The Model Application, An Area Of Approximately 10 Square Kilometers In The Center Of Beijing Is Selected To Demonstrate The Effectiveness Of The Proposed Model. The Results Indicated That The MPSL Model Could Effectively Analyze The Route Choice Behavior In Customer-searching Process And Express Higher Accuracy Than Traditional Multinomial Logit Model And Basic PSL Model.

FILE TRANSFER USING CRYPTOGRAPHIC TECHNIQUE - Enhancing Secure Digital Communication Media Using Cryptographic Steganography Techniques

Data Hiding Technique Is The Process Of Anti-computer Forensic For Making The Data Difficult To Accessible. Steganography Is Merging Texts, Files, Or Other Multimedia Files Within Another Texts, Files, Or Other Multimedia Files To Reduce The Visible Attack And It Is An Approach Of Data Hiding Technique. Cryptography Is Changing The Readable Text To Illegible Information. This Paper Presents About Secure Communication Media Which Is Used In Transferring Text, Multimedia Or Relevant Digital File Between Sender And Receiver Securely. To Have Securing Communication Media, The Media Required To Reduce The Possible Threats And Vulnerabilities. Therefore, Transferred Media Is Main Thing To Consideration For Having Communication System Firmly. Data Hiding Techniques Are Used To Improve The Security Of Communication Media Using Salt Encryption. This Paper Is Proposed The Methodology To Develop The Secure Communication Media Using Combination Of Cryptography And Steganography Techniques By Describing Experimental Results From Difference Technical Analysis.

PREDICT LENGTH OF STAY OF STROKE PATIENTS USING DATA MINING TECHNIQUES - SNOMED CT-Based Standardized E-Clinical Pathways For Enabling Big Data Analytics In Healthcare

Automation Of Healthcare Facilities Represents A Challenging Task Of Streamlining A Highly Information-intensive Sector. Modern Healthcare Processes Produce Large Amounts Of Data That Have Great Potential For Health Policymakers And Data Science Researchers. However, A Considerable Portion Of Such Data Is Not Captured In Electronic Format And Hidden Inside The Paperwork. A Major Source Of Missing Data In Healthcare Is Paper-based Clinical Pathways (CPs). CPs Are Healthcare Plans That Detail The Interventions For The Treatment Of Patients, And Thus Are The Primary Source For Healthcare Data. However, Most CPs Are Used As Paper-based Documents And Not Fully Automated. A Key Contribution Towards The Full Automation Of CPs Is Their Proper Computer Modeling And Encoding Their Data With International Clinical Terminologies. We Present In This Research An Ontology-based CP Automation Model In Which CP Data Are Standardized With SNOMED CT, Thus Enabling Machine Learning Algorithms To Be Applied To CP-based Datasets. CPs Automated Under This Model Contribute Significantly To Reducing Data Missingness Problems, Enabling Detailed Statistical Analyses On CP Data, And Improving The Results Of Data Analytics Algorithms. Our Experimental Results On Predicting The Length Of Stay (LOS) Of Stroke Patients Using A Dataset Resulting From An E-clinical Pathway Demonstrate Improved Prediction Results Compared With LOS Prediction Using Traditional EHR-based Datasets. Fully Automated CPs Enrich Medical Datasets With More CP Data And Open New Opportunities For Machine Learning Algorithms To Show Their Full Potential In Improving Healthcare, Reducing Costs, And Increasing Patient Satisfaction

PREDICT CHANGING STUDENTS ATTITUDE USING DATA MINING - Supporting Teachers To Monitor Students Learning Progress In An Educational Environment With Robotics Activities

Educational Robotics Has Proven Its Positive Impact On The Performances And Attitudes Of Students. However, The Educational Environments That Employ Them Rarely Provide Teachers With Relevant Information That Can Be Used To Make An Effective Monitoring Of The Student Learning Progress. To Overcome These Limitations, In This Paper We Present IDEE (Integrated Didactic Educational Environment), An Educational Environment For Physics, That Uses EV3 LEGO Mindstorms R Educational Kit As Robotic Component. To Provide Support To Teachers, IDEE Includes A Dashboard That Provides Them With Information About The Students’ Learning Process. This Analysis Is Done By Means Of An Additive Factor Model (AFM). That Is A Well-known Technique In The Educational Data Mining Research Area. However, It Has Been Usually Employed To Carry Out Analysis About Students’ Performance Data Outside The System. This Can Be A Burden For The Teacher Who, In Most Cases, Is Not An Expert In Data Analysis. Our Goal In This Paper Is To Show How The Coefficients Of AFM Provide Valuable Information To The Teacher Without Requiring Any Deep Expertise In Data Analysis. In Addition, We Show An Improved Version Of The AFM That Provides A Deeper Understanding About The Students’ Learning Process.

MALWARE DETECTION IN GOOGLE PLAY - Towards De-Anonymization Of Google Play Search Rank Fraud

Search Rank Fraud, The Fraudulent Promotion Of Products Hosted On Peer-review Sites, Is Driven By Expert Workers Recruited Online, Often From Crowdsourcing Sites. In This Paper We Introduce The Fraud De-anonymization Problem, That Goes Beyond Fraud Detection, To Unmask The Human Masterminds Responsible For Posting Search Rank Fraud In Peer-review Sites. We Collect And Study Data From Crowdsourced Search Rank Fraud Jobs, And Survey The Capabilities And Behaviors Of 58 Search Rank Fraud Workers Recruited From 6 Crowdsourcing Sites. We Collect A Gold Standard Dataset Of Google Play User Accounts Attributed To 23 Crowdsourced Workers And Analyze Their Fraudulent Behaviors In The Wild. We Propose Dolos , A Fraud De-anonymization System That Leverages Traits And Behaviors We Extract From Our Studies, To Attribute Detected Fraud To Crowdsourcing Site Workers, Thus To Real Identities And Bank Accounts. We Introduce MCDense, A Min-cut Dense Component Detection Algorithm To Uncover Groups Of User Accounts Controlled By Different Workers, And Use Stylometry And Supervised Learning To Attribute Them To Crowdsourcing Site Profiles. Dolos Correctly Identified The Owners Of 95 Percent Of Fraud Worker-controlled Communities, And Uncovered Fraud Workers Who Promoted As Many As 97.5 Percent Of Fraud Apps We Collected From Google Play. When Evaluated On 13,087 Apps (820,760 Reviews), Which We Monitored Over More Than 6 Months, Dolos Identified 1,056 Apps With Suspicious Reviewer Groups. We Report Orthogonal Evidence Of Their Fraud, Including Fraud Duplicates And Fraud Re-posts. Dolos Significantly Outperformed Adapted Dense Subgraph Detection And Loopy Belief Propagation Competitors, On Two New Coverage Scores That Measure The Quality Of Detected Community Partitions.

LOCATION AWARE KEYWORD QUERY SUGGESTION - Integrating “Random Forest” With Indexing And Query Processing For Personalized Search

The Internet Has Become An Integral Part Of At Least 4.4 Billion Lives. An Average Person Looks At Their Device At Least 20 Times A Day. One Can Only Imagine The Amount Of Queries A Search Engine Gets On A Daily Basis. With The Help Of All The Data Acquired Over The Years, The Internet Updates Us With All The Biggest Trends And Live Events Happening All Over The World. A Search Engine Is Able To Provide Query Suggestions Based On The Number Of Times A Keyword Has Been Searched For Or The Current Query Relates To A Certain Trend. All These Trends Are Updated To Every Device Internationally Or Locally. This Concept Is Generalized Throughout All Devices That Use Any Kind Of Search Engine On Any Application. Through This Paper We Intend To Propose To Use Random Forest As A Predictive Model To Be Integrated With The Indexing Process Of The Search Engine To Produce Query Suggestions That A User Would Want To Search, Contrary To The Query Suggestions That Are Usually Displayed Based On Hyped Trends And Fashion.

USER TRUST AND ITEM RATINGS PREDICT - A Novel Implicit Trust Recommendation Approach For Rating Prediction

Rating Predictions, As An Application That Is Widely Used In Recommender Systems, Have Gradually Become A Valuable Way Which Can Help User Narrow Down Their Choices Quickly And Make Wise Decisions From The Vast Amount Of Information. However, Most Existing Collaborative Recommendation Models Suffer From Poor Accuracy Due To Data Sparsity And Cold Start Problems That Recommender Systems Contain Only A Few Explicit Data. To Solve This Problem, A New Implicit Trust Recommendation Approach (ITRA) Is Proposed To Generate Item Rating Prediction By Mining And Utilizing User Implicit Information In Recommender Systems. Specifically, User Trust Neighbor Set That Has Similar Preference And Taste With A Target User Is First Obtained By Trust Expansion Strategy Via User Trust Diffusion Features In A Trust Network. Then, The Trust Ratings Mined From User Trust Neighbors Are Used To Compute Trust Similarity Among Users Based On User Collaborative Filtering Model. Finally, Using The Above Filtered Trust Ratings And User Trust Similarity, The Prediction Results Are Generated By A Trust Weighting Method. In Addition, The Empirical Experiments Are Conducted On Three Real-world Datasets, And The Results Demonstrate That Our Rating Prediction Model Has Obvious Advantages Over The State-of-the-art Comparison Methods In Terms Of The Accuracy Of Recommendations.

PRIVACY POLICY INFERENCE OF USER-UPLOADED IMAGES - User Flagging For Posts At 3DTubeorg The First Social Platform For 3D-Exclusive Contents

Social Networks Have Been A Popular Way For A Community To Share Content, Information, And News. Despite Section 230 Of The Communications Decency Act Of 1996 Protecting Social Platforms From Legal Liability Regarding User Uploaded Contents Of Their Platforms In The USA, There Has Been A Recent Call For Some Jurisdiction Over Platform Management Practices. This Duty Of Potential Jurisdiction Would Be Especially Challenging For Social Networks That Are Rich In Multimedia Contents, Such As 3DTube.org, Since 3D Capabilities Have A History Of Attracting Adult Materials And Other Controversial Content. This Paper Presents The Design Of 3DTube.org To Address Two Major Issues: (1) The Need For A Social Media Platform Of 3D Contents And (2) The Policies And Designs For Mediation Of Said Contents. Content Mediation Can Be Seen As A Compromise Between Two Conflicting Goals: Platform Micromanaging Of Content, Which Is Resource-intensive, And User Notification Of Flagged Content And Material, Prior To Viewing. This Paper Details 3DTube.org's Solution To Such A Compromise.

SEMANTICALLY SECURE ENCRYPTED RELATIONAL DATA USING K -NEAREST NEIGHBOR CLASSIFICATION - A Distributed Storage And Computation K-Nearest Neighbor Algorithm Based Cloud-Edge Computing For Cyber-Physical-Social Systems

The K-nearest Neighbor (kNN) Algorithm Is A Classic Supervised Machine Learning Algorithm. It Is Widely Used In Cyber-physical-social Systems (CPSS) To Analyze And Mine Data. However, In Practical CPSS Applications, The Standard Linear KNN Algorithm Struggles To Efficiently Process Massive Data Sets. This Paper Proposes A Distributed Storage And Computation K-nearest Neighbor (D-kNN) Algorithm. The D-kNN Algorithm Has The Following Advantages: First, The Concept Of K-nearest Neighbor Boundaries Is Proposed And The K-nearest Neighbor Search Within The K-nearest Neighbors Boundaries Can Effectively Reduce The Time Complexity Of KNN. Second, Based On The K-neighbor Boundary, Massive Data Sets Beyond The Main Storage Space Are Stored On Distributed Storage Nodes. Third, The Algorithm Performs K-nearest Neighbor Searching Efficiently By Performing Distributed Calculations At Each Storage Node. Finally, A Series Of Experiments Were Performed To Verify The Effectiveness Of The D-kNN Algorithm. The Experimental Results Show That The D-kNN Algorithm Based On Distributed Storage And Calculation Effectively Improves The Operation Efficiency Of K-nearest Neighbor Search. The Algorithm Can Be Easily And Flexibly Deployed In A Cloud-edge Computing Environment To Process Massive Data Sets In CPSS.

COMPLICATION RISK PROFILING IN DIABETES CARE- A Bayesian Multi-Task And Feature Relationship Learning Approach

Diabetes Mellitus, Commonly Known As Diabetes, Is A Chronic Disease That Often Results In Multiple Complications. Risk Prediction Of Diabetes Complications Is Critical For Healthcare Professionals To Design Personalized Treatment Plans For Patients In Diabetes Care For Improved Outcomes. In This Paper, Focusing On Type 2 Diabetes Mellitus (T2DM), We Study The Risk Of Developing Complications After The Initial T2DM Diagnosis From Longitudinal Patient Records. We Propose A Novel Multi-task Learning Approach To Simultaneously Model Multiple Complications Where Each Task Corresponds To The Risk Modeling Of One Complication. Specifically, The Proposed Method Strategically Captures The Relationships (1) Between The Risks Of Multiple T2DM Complications, (2) Between Different Risk Factors, And (3) Between The Risk Factor Selection Patterns, Which Assumes Similar Complications Have Similar Contributing Risk Factors. The Method Uses Coefficient Shrinkage To Identify An Informative Subset Of Risk Factors From High-dimensional Data, And Uses A Hierarchical Bayesian Framework To Allow Domain Knowledge To Be Incorporated As Priors. The Proposed Method Is Favorable For Healthcare Applications Because In Addition To Improved Prediction Performance, Relationships Among The Different Risks And Among Risk Factors Are Also Identified. Extensive Experimental Results On A Large Electronic Medical Claims Database Show That The Proposed Method Outperforms State-of-the-art Models By A Significant Margin. Furthermore, We Show That The Risk Associations Learned And The Risk Factors Identified Lead To Meaningful Clinical Insights.

SUPPLY AND DEMAND CHAIN INTEGRATION- Sustainable Supply And Demand Chain Integration Within Global Manufacturing Industries

Given The Emerging Industrial Management Strategies Considering Three Pillars Of Sustainability In Particular, There Is A Vital Need To Determine The Differences Of Sustainability Practices Within Both Supply And Demand Distribution Systems Through Global Manufacturing Environments Providing With The Successful Global Trade And Logistics. This Research Paper Aims To Explore The Interactions And Advantages Of Sustainability Applications Within Both Supply And Demand Chain Management. The Research Framework Adopted Consists Of Survey Questionnaire Method Which Is Conducted Within A Global Tyre Manufacturing Company. The Research Results And Analysis Justify The Need For The Application Of Ethical Codes, Supply Chain Transformation And The Effective Association Of Industry Executives, Professional Bodies And The Government. The Research Study Also Identifies That The Vital Incentive Factors For The Organisation Towards Sustainable Supply Demand Chain (SSDC) Are Mostly The Financial Benefits Of Doing So And Therefore, A Positive Mind-set Shift Towards Greening Practices Is Required.

MULTI-KEY WORD RANKED SEARCH - Privacy Preserving Multi-Key Word Ranked Search Over Encrypted Cloud Data

With The Advent Of Cloud Computing, Data Owners Are Motivated To Outsource Their Complex Data Management Systems From Local Sites To The Commercial Public Cloud For Great Flexibility And Economic Savings. But For Protecting Data Privacy, Sensitive Data Have To Be Encrypted Before Outsourcing, Which Obsoletes Traditional Data Utilization Based On Plaintext Keyword Search. Thus, Enabling An Encrypted Cloud Data Search Service Is Of Paramount Importance. Considering The Large Number Of Data Users And Documents In The Cloud, It Is Necessary To Allow Multiple Keywords In The Search Request And Return Documents In The Order Of Their Relevance To These Keywords. Related Works On Searchable Encryption Focus On Single Keyword Search Or Boolean Keyword Search, And Rarely Sort The Search Results. In This Paper, For The First Time, We Define And Solve The Challenging Problem Of Privacy-preserving Multi-keyword Ranked Search Over Encrypted Data In Cloud Computing (MRSE). We Establish A Set Of Strict Privacy Requirements For Such A Secure Cloud Data Utilization System. Among Various Multi-keyword Semantics, We Choose The Efficient Similarity Measure Of "coordinate Matching," I.e., As Many Matches As Possible, To Capture The Relevance Of Data Documents To The Search Query. We Further Use "inner Product Similarity" To Quantitatively Evaluate Such Similarity Measure. We First Propose A Basic Idea For The MRSE Based On Secure Inner Product Computation, And Then Give Two Significantly Improved MRSE Schemes To Achieve Various Stringent Privacy Requirements In Two Different Threat Models. To Improve Search Experience Of The Data Search Service, We Further Extend These Two Schemes To Support More Search Semantics. Thorough Analysis Investigating Privacy And Efficiency Guarantees Of Proposed Schemes Is Given. Experiments On The Real-world Data Set Further Show Proposed Schemes Indeed Introduce Low Overhead On Computation And Communication.

SKYLINE PRODUCT - Finding Optimal Skyline Product Combination Under Price Promotion

Nowadays, With The Development Of E-commerce, A Growing Number Of Customers Choose To Go Shopping Online. To Find Attractive Products From Online Shopping Marketplaces, The Skyline Query Is A Useful Tool Which Offers More Interesting And Preferable Choices For Customers. The Skyline Query And Its Variants Have Been Extensively Investigated. However, To The Best Of Our Knowledge, They Have Not Taken Into Account The Requirements Of Customers In Certain Practical Application Scenarios. Recently, Online Shopping Marketplaces Usually Hold Some Price Promotion Campaigns To Attract Customers And Increase Their Purchase Intention. Considering The Requirements Of Customers In This Practical Application Scenario, We Are Concerned About Product Selection Under Price Promotion. We Formulate A Constrained Optimal Product Combination (COPC) Problem. It Aims To Find Out The Skyline Product Combinations Which Both Meet A Customer's Willingness To Pay And Bring The Maximum Discount Rate. The COPC Problem Is Significant To Offer Powerful Decision Support For Customers Under Price Promotion, Which Is Certified By A Customer Study. To Process The COPC Problem Effectively, We First Propose A Two List Exact (TLE) Algorithm. The COPC Problem Is Proven To Be NP-hard, And The TLE Algorithm Is Not Scalable Because It Needs To Process An Exponential Number Of Product Combinations. Additionally, We Design A Lower Bound Approximate (LBA) Algorithm That Has A Guarantee About The Accuracy Of The Results And An Incremental Greedy (IG) Algorithm That Has Good Performance. The Experiment Results Demonstrate The Efficiency And Effectiveness Of Our Proposed Algorithms.

SENTIMENTAL ANALYSIS- Age Related Sentimental Analysis For Efficient Review Mining

Natural Language Processing Has Been Continuous Field Of Interest Since 1950s. It Is Concerned With The Interaction Between Computers And Human’s Natural Languages. The History Of Natural Language Processing Started With Alan Turing’s Article Titled “Computer Machinery And Intelligence”. How Natural Language Is Processed By Computers Is Main Concern Of NLP. Speech Recognition, Text Analysis, Text Translation Are Few Areas Where Natural Language Processing Along With Artificial Intelligence Is Employed. NLP Includes Various Evaluation Tasks Such As Stemming, Grammar Induction, Topic Segmentation Etc. This Project Aims At Developing A Program That Is Used For Age Related Sentiment Analysis. Sentiment Analysis Refers To The Use Of Natural Language Processing, Text Analysis, Computational Linguistics, And Biometrics To Systematically Identify, Extract, Quantify, And Study Affective States And Subjective Information. Methods To Approach Sentiment Analysis Are Classified Mainly Into Knowledge Based Approach, Statistical Approach And Hybrid Approach. Provided A Text, Mood Of The Text Will Be Analysed. The Main Constraint That Is Applied Here Is Age. The Text Will Be Analysed Related To The Age. The Opinion Or Mood Behind The Particular Text Varies For Every Age Group Since Their Understanding Levels And Conceptual Knowledge Varies. Word Ambiguity Is Analysed And Based On The Keyword Detection And Context Analysis Ambiguity Is Removed. Age Is Taken Into Consideration While Analysing The Text And Hence For The Same Text In The Same Context Analysis Varies.

GEOGRAPHICAL PROBABILISTIC FACTOR MODEL - A General Geographical Probabilistic Factor Model For Point Of Interest Recommendation

The Problem Of Point Of Interest (POI) Recommendation Is To Provide Personalized Recommendations Of Places, Such As Restaurants And Movie Theaters. The Increasing Prevalence Of Mobile Devices And Of Location Based Social Networks (LBSNs) Poses Significant New Opportunities As Well As Challenges, Which We Address. The Decision Process For A User To Choose A POI Is Complex And Can Be Influenced By Numerous Factors, Such As Personal Preferences, Geographical Considerations, And User Mobility Behaviors. This Is Further Complicated By The Connection LBSNs And Mobile Devices. While There Are Some Studies On POI Recommendations, They Lack An Integrated Analysis Of The Joint Effect Of Multiple Factors. Meanwhile, Although Latent Factor Models Have Been Proved Effective And Are Thus Widely Used For Recommendations, Adopting Them To POI Recommendations Requires Delicate Consideration Of The Unique Characteristics Of LBSNs. To This End, In This Paper, We Propose A General Geographical Probabilistic Factor Model (Geo-PFM) Framework Which Strategically Takes Various Factors Into Consideration. Specifically, This Framework Allows To Capture The Geographical Influences On A User's Check-in Behavior. Also, User Mobility Behaviors Can Be Effectively Leveraged In The Recommendation Model. Moreover, Based Our Geo-PFM Framework, We Further Develop A Poisson Geo-PFM Which Provides A More Rigorous Probabilistic Generative Process For The Entire Model And Is Effective In Modeling The Skewed User Check-in Count Data As Implicit Feedback For Better POI Recommendations. Finally, Extensive Experimental Results On Three Real-world LBSN Datasets (which Differ In Terms Of User Mobility, POI Geographical Distribution, Implicit Response Data Skewness, And User-POI Observation Sparsity), Show That The Proposed Recommendation Methods Outperform State-of-the-art Latent Factor Models By A Significant Margin

SCALABLE GRAPH-BASED RANKING MODEL- EMR A Scalable Graph-based Ranking Model For Content-based Image Retrieval

Graph-based Ranking Models Have Been Widely Applied In Information Retrieval Area. In This Paper, We Focus On A Well Known Graph-based Model - The Ranking On Data Manifold Model, Or Manifold Ranking. Particularly, It Has Been Successfully Applied To Content-based Image Retrieval, Because Of Its Outstanding Ability To Discover Underlying Geometrical Structure Of The Given Image Database. However, Manifold Ranking Is Computationally Very Expensive, Which Significantly Limits Its Applicability To Large Databases Especially For The Cases That The Queries Are Out Of The Database. We Propose A Novel Scalable Graph-based Ranking Model Called Efficient Manifold Ranking (EMR), Trying To Address The Shortcomings Of MR From Two Main Perspectives: Scalable Graph Construction And Efficient Ranking Computation. Specifically, We Build An Anchor Graph On The Database Instead Of A Traditional K-nearest Neighbor Graph, And Design A New Form Of Adjacency Matrix Utilized To Speed Up The Ranking. An Approximate Method Is Adopted For Efficient Out-of-sample Retrieval. Experimental Results On Some Large Scale Image Databases Demonstrate That EMR Is A Promising Method For Real World Retrieval Applications.

ROUTE-SAVER- Leveraging Route APIs For Accurate And Efficient Query Processing At Location Based Services

Location-based Services (LBS) Enable Mobile Users To Query Points-of-interest (e.g., Restaurants, Cafes) On Various Features (e.g., Price, Quality, Variety). In Addition, Users Require Accurate Query Results With Up-to-date Travel Times. Lacking The Monitoring Infrastructure For Road Traffic, The LBS May Obtain Live Travel Times Of Routes From Online Route APIs In Order To Offer Accurate Results. Our Goal Is To Reduce The Number Of Requests Issued By The LBS Significantly While Preserving Accurate Query Results. First, We Propose To Exploit Recent Routes Requested From Route APIs To Answer Queries Accurately. Then, We Design Effective Lower/upper Bounding Techniques And Ordering Techniques To Process Queries Efficiently. Also, We Study Parallel Route Requests To Further Reduce The Query Response Time. Our Experimental Evaluation Shows That Our Solution Is Three Times More Efficient Than A Competitor, And Yet Achieves High Result Accuracy (above 98 Percent).

TWEET SEGMENTATION - Tweet Segmentation And Its Application To Named Entity Recognition

Twitter Has Attracted Millions Of Users To Share And Disseminate Most Up-to-date Information, Resulting In Large Volumes Of Data Produced Everyday. However, Many Applications In Information Retrieval (IR) And Natural Language Processing (NLP) Suffer Severely From The Noisy And Short Nature Of Tweets. In This Paper, We Propose A Novel Framework For Tweet Segmentation In A Batch Mode, Called HybridSeg. By Splitting Tweets Into Meaningful Segments, The Semantic Or Context Information Is Well Preserved And Easily Extracted By The Downstream Applications. HybridSeg Finds The Optimal Segmentation Of A Tweet By Maximizing The Sum Of The Stickiness Scores Of Its Candidate Segments. The Stickiness Score Considers The Probability Of A Segment Being A Phrase In English (i.e., Global Context) And The Probability Of A Segment Being A Phrase Within The Batch Of Tweets (i.e., Local Context). For The Latter, We Propose And Evaluate Two Models To Derive Local Context By Considering The Linguistic Features And Term-dependency In A Batch Of Tweets, Respectively. HybridSeg Is Also Designed To Iteratively Learn From Confident Segments As Pseudo Feedback. Experiments On Two Tweet Data Sets Show That Tweet Segmentation Quality Is Significantly Improved By Learning Both Global And Local Contexts Compared With Using Global Context Alone. Through Analysis And Comparison, We Show That Local Linguistic Features Are More Reliable For Learning Local Context Compared With Term-dependency. As An Application, We Show That High Accuracy Is Achieved In Named Entity Recognition By Applying Segment-based Part-of-speech (POS) Tagging.

PRIVACY AND DATA CONFIDENTIALITY - Fast A Fast Clustering-Based Database With Privacy And Data Confidentiality

In Order To Prevent The Disclosure Of Sensitive Information And Protect Users' Privacy, The Generalization And Suppression Of Technology Is Often Used To Anonymize The Quasi-identifiers Of The Data Before Its Sharing. Data Streams Are Inherently Infinite And Highly Dynamic Which Are Very Different From Static Datasets, So That The Anonymization Of Data Streams Needs To Be Capable Of Solving More Complicated Problems. The Methods For Anonymizing Static Datasets Cannot Be Applied To Data Streams Directly. In This Paper, An Anonymization Approach For Data Streams Is Proposed With The Analysis Of The Published Anonymization Methods For Data Streams. This Approach Scans The Data Only Once To Recognize And Reuse The Clusters That Satisfy The Anonymization Requirements For Speeding Up The Anonymization Process. Experimental Results On The Real Dataset Show That The Proposed Method Can Reduce The Information Loss That Is Caused By Generalization And Suppression And Also Satisfies The Anonymization Requirements And Has Low Time And Space Complexity.

INSTANT MESSAGE USING DATA MINING AND ONTOLOGY- Framework For Survelliance Of Instant Messages In Instant Messengers And Social Networking Sites Using Data Mining And Ontology

Innumerable Terror And Suspicious Messages Are Sent Through Instant Messengers (IM) And Social Networking Sites (SNS) Which Are Untraced, Leading To Hindrance For Network Communications And Cyber Security. We Propose A Framework That Discover And Predict Such Messages That Are Sent Using IM Or SNS Like Facebook, Twitter, LinkedIn, And Others. Further, These Instant Messages Are Put Under Surveillance That Identifies The Type Of Suspected Cyber Threat Activity By Culprit Along With Their Personnel Details. Framework Is Developed Using Ontology Based Information Extraction Technique (OBIE), Association Rule Mining (ARM) A Data Mining Technique With Set Of Pre-defined Knowledge-based Rules (logical), For Decision Making Process That Are Learned From Domain Experts And Past Learning Experiences Of Suspicious Dataset Like GTD (Global Terrorist Database). The Experimental Results Obtained Will Aid To Take Prompt Decision For Eradicating Cyber Crimes.

WEB SERVICES RECOMMENDATION - Diversifying Web Service Recommendation Results Via Exploring Service Usage History

The Last Decade Has Witnessed A Tremendous Growth Of Web Services As A Major Technology For Sharing Data, Computing Resources, And Programs On The Web. With The Increasing Adoption And Presence Of Web Services, Design Of Novel Approaches For Effective Web Service Recommendation To Satisfy Users’ Potential Requirements Has Become Of Paramount Importance. Existing Web Service Recommendation Approaches Mainly Focus On Predicting Missing QoS Values Of Web Service Candidates Which Are Interesting To A User Using Collaborative Filtering Approach, Content-based Approach, Or Their Hybrid. These Recommendation Approaches Assume That Recommended Web Services Are Independent To Each Other, Which Sometimes May Not Be True. As A Result, Many Similar Or Redundant Web Services May Exist In A Recommendation List. In This Paper, We Propose A Novel Web Service Recommendation Approach Incorporating A User's Potential QoS Preferences And Diversity Feature Of User Interests On Web Services. User's Interests And QoS Preferences On Web Services Are First Mined By Exploring The Web Service Usage History. Then We Compute Scores Of Web Service Candidates By Measuring Their Relevance With Historical And Potential User Interests, And Their QoS Utility. We Also Construct A Web Service Graph Based On The Functional Similarity Between Web Services. Finally, We Present An Innovative Diversity-aware Web Service Ranking Algorithm To Rank The Web Service Candidates Based On Their Scores, And Diversity Degrees Derived From The Web Service Graph. Extensive Experiments Are Conducted Based On A Real World Web Service Dataset, Indicating That Our Proposed Web Service Recommendation Approach Significantly Improves The Quality Of The Recommendation Results Compared With Existing Methods.

DATA RETRIEVAL PROCESS- Generating Boolean Matrix For Data Retrieval Process

An Data Retrieval (DR) Or Information Retrieval (IR) Process Begins When A User Enters A Query Into The System. Queries Are Formal Statements Of Information Needs, For Example Search Strings In Web Search Engines. In IR A Query Does Not Uniquely Identify A Single Object In The Collection. Instead, Several Objects May Match The Query, Perhaps With Different Degrees Of Relevancy. An Object Is An Entity Which Keeps Or Stores Information In A Database. User Queries Are Matched To Objects Stored In The Database. Depending On The Application The Data Objects May Be, For Example, Text Documents, Images Or Videos. The Documents Themselves Are Not Kept Or Stored Directly In The IR System, But Are Instead Represented In The System By Document Surrogates. Most IR Systems Compute A Numeric Score On How Well Each Objects In The Database Match The Query, And Rank The Objects According To This Value. The Top Ranking Objects Are Then Shown To The User. The Process May Then Be Iterated If The User Wishes To Refine The Query. In This Paper We Try To Explain IR Methods And Asses Them From Two View Points And Finally Propose A Simple Method For Ranking Terms And Documents On IR And Implement The Method And Check The Result.

UNCERTAIN OBJECT- Query Aware Determinization Of Uncertain Objects

This Paper Considers The Problem Of Determinizing Probabilistic Data To Enable Such Data To Be Stored In Legacy Systems That Accept Only Deterministic Input. Probabilistic Data May Be Generated By Automated Data Analysis/enrichment Techniques Such As Entity Resolution, Information Extraction, And Speech Processing. The Legacy System May Correspond To Pre-existing Web Applications Such As Flickr, Picasa, Etc. The Goal Is To Generate A Deterministic Representation Of Probabilistic Data That Optimizes The Quality Of The End-application Built On Deterministic Data. We Explore Such A Determinization Problem In The Context Of Two Different Data Processing Tasks-triggers And Selection Queries. We Show That Approaches Such As Thresholding Or Top-1 Selection Traditionally Used For Determinization Lead To Suboptimal Performance For Such Applications. Instead, We Develop A Query-aware Strategy And Show Its Advantages Over Existing Solutions Through A Comprehensive Empirical Evaluation Over Real And Synthetic Datasets.

EFFECTIVE AND EFFICIENT CLUSTERING METHOD - Effective And Efficient Clustering Methods For Correlated Probabilistic Graphs

Recently, Probabilistic Graphs Have Attracted Significant Interests Of The Data Mining Community. It Is Observed That Correlations May Exist Among Adjacent Edges In Various Probabilistic Graphs. As One Of The Basic Mining Techniques, Graph Clustering Is Widely Used In Exploratory Data Analysis, Such As Data Compression, Information Retrieval, Image Segmentation, Etc. Graph Clustering Aims To Divide Data Into Clusters According To Their Similarities, And A Number Of Algorithms Have Been Proposed For Clustering Graphs, Such As The PKwikCluster Algorithm, Spectral Clustering, K-path Clustering, Etc. However, Little Research Has Been Performed To Develop Efficient Clustering Algorithms For Probabilistic Graphs. Particularly, It Becomes More Challenging To Efficiently Cluster Probabilistic Graphs When Correlations Are Considered. In This Paper, We Define The Problem Of Clustering Correlated Probabilistic Graphs. To Solve The Challenging Problem, We Propose Two Algorithms, Namely The PEEDR And The CPGS Clustering Algorithm. For Each Of The Proposed Algorithms, We Develop Several Pruning Techniques To Further Improve Their Efficiency. We Evaluate The Effectiveness And Efficiency Of Our Algorithms And Pruning Methods Through Comprehensive Experiments.

DOCUMENT ANNOTATION UISNG CONTENT AND QUERYING VALUE- Facilitating Document Annotation Using Content And Querying Value

A Large Number Of Organizations Today Generate And Share Textual Descriptions Of Their Products, Services, And Actions. Such Collections Of Textual Data Contain Significant Amount Of Structured Information, Which Remains Buried In The Unstructured Text. While Information Extraction Algorithms Facilitate The Extraction Of Structured Relations, They Are Often Expensive And Inaccurate, Especially When Operating On Top Of Text That Does Not Contain Any Instances Of The Targeted Structured Information. We Present A Novel Alternative Approach That Facilitates The Generation Of The Structured Metadata By Identifying Documents That Are Likely To Contain Information Of Interest And This Information Is Going To Be Subsequently Useful For Querying The Database. Our Approach Relies On The Idea That Humans Are More Likely To Add The Necessary Metadata During Creation Time, If Prompted By The Interface; Or That It Is Much Easier For Humans (and/or Algorithms) To Identify The Metadata When Such Information Actually Exists In The Document, Instead Of Naively Prompting Users To Fill In Forms With Information That Is Not Available In The Document. As A Major Contribution Of This Paper, We Present Algorithms That Identify Structured Attributes That Are Likely To Appear Within The Document, By Jointly Utilizing The Content Of The Text And The Query Workload. Our Experimental Evaluation Shows That Our Approach Generates Superior Results Compared To Approaches That Rely Only On The Textual Content Or Only On The Query Workload, To Identify Attributes Of Interest.

PRIVACY PROTECTION - Supporting Privacy Protection In Personalized Web Search

Personalized Web Search (PWS) Has Demonstrated Its Effectiveness In Improving The Quality Of Various Search Services On The Internet. However, Evidences Show That Users' Reluctance To Disclose Their Private Information During Search Has Become A Major Barrier For The Wide Proliferation Of PWS. We Study Privacy Protection In PWS Applications That Model User Preferences As Hierarchical User Profiles. We Propose A PWS Framework Called UPS That Can Adaptively Generalize Profiles By Queries While Respecting User-specified Privacy Requirements. Our Runtime Generalization Aims At Striking A Balance Between Two Predictive Metrics That Evaluate The Utility Of Personalization And The Privacy Risk Of Exposing The Generalized Profile. We Present Two Greedy Algorithms, Namely GreedyDP And GreedyIL, For Runtime Generalization. We Also Provide An Online Prediction Mechanism For Deciding Whether Personalizing A Query Is Beneficial. Extensive Experiments Demonstrate The Effectiveness Of Our Framework. The Experimental Results Also Reveal That GreedyIL Significantly Outperforms GreedyDP In Terms Of Efficiency.

TRUSTEDDB- A Trusted Hardware-Based Database With Privacy And Data Confidentiality

Traditionally, As Soon As Confidentiality Becomes A Concern, Data Are Encrypted Before Outsourcing To A Service Provider. Any Software-based Cryptographic Constructs Then Deployed, For Server-side Query Processing On The Encrypted Data, Inherently Limit Query Expressiveness. Here, We Introduce TrustedDB, An Outsourced Database Prototype That Allows Clients To Execute SQL Queries With Privacy And Under Regulatory Compliance Constraints By Leveraging Server-hosted, Tamper-proof Trusted Hardware In Critical Query Processing Stages, Thereby Removing Any Limitations On The Type Of Supported Queries. Despite The Cost Overhead And Performance Limitations Of Trusted Hardware, We Show That The Costs Per Query Are Orders Of Magnitude Lower Than Any (existing Or) Potential Future Software-only Mechanisms. TrustedDB Is Built And Runs On Actual Hardware, And Its Performance And Costs Are Evaluated Here.

FAST CLUSTERING- A Fast Clustering-Based Feature Subset Selection Algorithm For High-Dimensional Data

Feature Selection Involves Identifying A Subset Of The Most Useful Features That Produces Compatible Results As The Original Entire Set Of Features. A Feature Selection Algorithm May Be Evaluated From Both The Efficiency And Effectiveness Points Of View. While The Efficiency Concerns The Time Required To Find A Subset Of Features, The Effectiveness Is Related To The Quality Of The Subset Of Features. Based On These Criteria, A Fast Clustering-based Feature Selection Algorithm (FAST) Is Proposed And Experimentally Evaluated In This Paper. The FAST Algorithm Works In Two Steps. In The First Step, Features Are Divided Into Clusters By Using Graph-theoretic Clustering Methods. In The Second Step, The Most Representative Feature That Is Strongly Related To Target Classes Is Selected From Each Cluster To Form A Subset Of Features. Features In Different Clusters Are Relatively Independent, The Clustering-based Strategy Of FAST Has A High Probability Of Producing A Subset Of Useful And Independent Features. To Ensure The Efficiency Of FAST, We Adopt The Efficient Minimum-spanning Tree (MST) Clustering Method. The Efficiency And Effectiveness Of The FAST Algorithm Are Evaluated Through An Empirical Study. Extensive Experiments Are Carried Out To Compare FAST And Several Representative Feature Selection Algorithms, Namely, FCBF, ReliefF, CFS, Consist, And FOCUS-SF, With Respect To Four Types Of Well-known Classifiers, Namely, The Probability-based Naive Bayes, The Tree-based C4.5, The Instance-based IB1, And The Rule-based RIPPER Before And After Feature Selection. The Results, On 35 Publicly Available Real-world High-dimensional Image, Microarray, And Text Data, Demonstrate That The FAST Not Only Produces Smaller Subsets Of Features But Also Improves The Performances Of The Four Types Of Classifiers.

DISTRIBUTED PROCESSING - Distributed Processing Of Probabilistic Top-k Queries In Wireless Sensor Networks

In This Paper, We Introduce The Notion Of Sufficient Set And Necessary Set For Distributed Processing Of Probabilistic Top-k Queries In Cluster-based Wireless Sensor Networks. These Two Concepts Have Very Nice Properties That Can Facilitate Localized Data Pruning In Clusters. Accordingly, We Develop A Suite Of Algorithms, Namely, Sufficient Set-based (SSB), Necessary Set-based (NSB), And Boundary-based (BB), For Intercluster Query Processing With Bounded Rounds Of Communications. Moreover, In Responding To Dynamic Changes Of Data Distribution In The Network, We Develop An Adaptive Algorithm That Dynamically Switches Among The Three Proposed Algorithms To Minimize The Transmission Cost. We Show The Applicability Of Sufficient Set And Necessary Set To Wireless Sensor Networks With Both Two-tier Hierarchical And Tree-structured Network Topologies. Experimental Results Show That The Proposed Algorithms Reduce Data Transmissions Significantly And Incur Only Small Constant Rounds Of Data Communications. The Experimental Results Also Demonstrate The Superiority Of The Adaptive Algorithm, Which Achieves A Near-optimal Performance Under Various Conditions.

XML RETRIEVAL - Using Personalization To Improve XML Retrieval

As The Amount Of Information Increases Every Day And The Users Normally Formulate Short And Ambiguous Queries, Personalized Search Techniques Are Becoming Almost A Must. Using The Information About The User Stored In A User Profile, These Techniques Retrieve Results That Are Closer To The User Preferences. On The Other Hand, The Information Is Being Stored More And More In An Semi-structured Way, And XML Has Emerged As A Standard For Representing And Exchanging This Type Of Data. XML Search Allows A Higher Retrieval Effectiveness, Due To Its Ability To Retrieve And To Show The User Specific Parts Of The Documents Instead Of The Full Document. In This Paper We Propose Several Personalization Techniques In The Context Of XML Retrieval. We Try To Combine The Different Approaches Where Personalization May Be Applied: Query Reformulation, Re-ranking Of Results And Retrieval Model Modification. The Experimental Results Obtained From A User Study Using A Parliamentary Document Collection Support The Validity Of Our Approach.

ASSOCIATION RULE AND THE APRIORI ALGORTHIM- A Data Mining Project -Discovering Association Rules Using The Apriori Algorithm

Data Mining Has A Lot Of E-Commerce Applications. The Key Problem Is How To Find Useful Hidden Patterns For Better Business Applications In The Retail Sector. For The Solution Of These Problems, The Apriori Algorithm Is One Of The Most Popular Data Mining Approaches For Finding Frequent Item Sets From A Transaction Dataset And Derives Association Rules. Rules Are The Discovered Knowledge From The Data Base. Finding Frequent Item Set (item Sets With Frequency Larger Than Or Equal To A User Specified Minimum Support) Is Not Trivial Because Of Its Combinatorial Explosion. Once Frequent Item Sets Are Obtained, It Is Straightforward To Generate Association Rules With Confidence Larger Than Or Equal To A User Specified Minimum Confidence. The Paper Illustrating Apriori Algorithm On Simulated Database And Finds The Association Rules On Different Confidence Value.

TEXT CLASSIFICATION AND CLUSTERING- Similarity Measure For Text Classification And Clustering

Measuring The Similarity Between Documents Is An Important Operation In The Text Processing Field. In This Paper, A New Similarity Measure Is Proposed. To Compute The Similarity Between Two Documents With Respect To A Feature, The Proposed Measure Takes The Following Three Cases Into Account: A) The Feature Appears In Both Documents, B) The Feature Appears In Only One Document, And C) The Feature Appears In None Of The Documents. For The First Case, The Similarity Increases As The Difference Between The Two Involved Feature Values Decreases. Furthermore, The Contribution Of The Difference Is Normally Scaled. For The Second Case, A Fixed Value Is Contributed To The Similarity. For The Last Case, The Feature Has No Contribution To The Similarity. The Proposed Measure Is Extended To Gauge The Similarity Between Two Sets Of Documents. The Effectiveness Of Our Measure Is Evaluated On Several Real-world Data Sets For Text Classification And Clustering Problems. The Results Show That The Performance Obtained By The Proposed Measure Is Better Than That Achieved By Other Measures.

ASSOCIATION RULE MINING - An Efficient Multi-Party Communication Scheme With Association Rule Mining

A Protocol For Secure Mining Of Association Rules In Horizontally Distributed Databases. Our Protocol, Like Theirs, Is Based On The Fast Distributed Mining (FDM) Algorithm Which Is An Unsecured Distributed Version Of The Apriori Algorithm. The Main Ingredients In Our Protocol Are Two Novel Secure Multi-party Algorithms One That Computes The Union Of Private Subsets That Each Of The Interacting Players Hold, And Another That Tests The Inclusion Of An Element Held By One Player In A Subset Held By Another. Our Protocol Offers Enhanced Privacy With Respect To The Protocol. In Addition, It Is Simpler And Is Significantly More Efficient In Terms Of Communication Rounds, Communication Cost And Computational Cost.

DUPLICATE DETECTION- Progressive Duplicate Detection

Duplicate Detection Is The Process Of Identifying Multiple Representations Of Same Real World Entities. Today, Duplicate Detection Methods Need To Process Ever Larger Datasets In Ever Shorter Time: Maintaining The Quality Of A Dataset Becomes Increasingly Difficult. We Present Two Novel, Progressive Duplicate Detection Algorithms That Significantly Increase The Efficiency Of Finding Duplicates If The Execution Time Is Limited: They Maximize The Gain Of The Overall Process Within The Time Available By Reporting Most Results Much Earlier Than Traditional Approaches. Comprehensive Experiments Show That Our Progressive Algorithms Can Double The Efficiency Over Time Of Traditional Duplicate Detection And Significantly Improve Upon Related Work.

RRW- A Robust And Reversible Watermarking Technique For Relational Data

Advancement In Information Technology Is Playing An Increasing Role In The Use Of Information Systems Comprising Relational Databases. These Databases Are Used Effectively In Collaborative Environments For Information Extraction; Consequently, They Are Vulnerable To Security Threats Concerning Ownership Rights And Data Tampering. Watermarking Is Advocated To Enforce Ownership Rights Over Shared Relational Data And For Providing A Means For Tackling Data Tampering. When Ownership Rights Are Enforced Using Watermarking, The Underlying Data Undergoes Certain Modifications; As A Result Of Which, The Data Quality Gets Compromised. Reversible Watermarking Is Employed To Ensure Data Quality Along-with Data Recovery. However, Such Techniques Are Usually Not Robust Against Malicious Attacks And Do Not Provide Any Mechanism To Selectively Watermark A Particular Attribute By Taking Into Account Its Role In Knowledge Discovery. Therefore, Reversible Watermarking Is Required That Ensures; (i) Watermark Encoding And Decoding By Accounting For The Role Of All The Features In Knowledge Discovery; And, (ii) Original Data Recovery In The Presence Of Active Malicious Attacks. In This Paper, A Robust And Semi-blind Reversible Watermarking (RRW) Technique For Numerical Relational Data Has Been Proposed That Addresses The Above Objectives. Experimental Studies Prove The Effectiveness Of RRW Against Malicious Attacks And Show That The Proposed Technique Outperforms Existing Ones.

PRIVACY PRESERVING SOCIAL MEDIA DATA PUBLISHING- Privacy Preserving Social Media Data Publishing For Personalized Rank Based Recommendation

Personalized Recommendation Is Crucial To Help Users Find Pertinent Information. It Often Relies On A Large Collection Of User Data, In Particular Users' Online Activity (e.g., Tagging/rating/checking-in) On Social Media, To Mine User Preference. However, Releasing Such User Activity Data Makes Users Vulnerable To Inference Attacks, As Private Data (e.g., Gender) Can Often Be Inferred From The Users' Activity Data. In This Paper, We Proposed PrivRank, A Customizable And Continuous Privacy-preserving Social Media Data Publishing Framework Protecting Users Against Inference Attacks While Enabling Personalized Ranking-based Recommendations. Its Key Idea Is To Continuously Obfuscate User Activity Data Such That The Privacy Leakage Of User-specified Private Data Is Minimized Under A Given Data Distortion Budget, Which Bounds The Ranking Loss Incurred From The Data Obfuscation Process In Order To Preserve The Utility Of The Data For Enabling Recommendations. An Empirical Evaluation On Both Synthetic And Real-world Datasets Shows That Our Framework Can Efficiently Provide Effective And Continuous Protection Of User-specified Private Data, While Still Preserving The Utility Of The Obfuscated Data For Personalized Ranking-based Recommendation. Compared To State-of-the-art Approaches, PrivRank Achieves Both A Better Privacy Protection And A Higher Utility In All The Ranking-based Recommendation Use Cases We Tested

ACTIVE LEARNING FOR RANKING - Active Learning For Ranking Through Expected Loss Optimization

Learning To Rank Arises In Many Data Mining Applications, Ranging From Web Search Engine, Online Advertising To Recommendation System. In Learning To Rank, The Performance Of A Ranking Model Is Strongly Affected By The Number Of Labeled Examples In The Training Set; On The Other Hand, Obtaining Labeled Examples For Training Data Is Very Expensive And Time-consuming. This Presents A Great Need For The Active Learning Approaches To Select Most Informative Examples For Ranking Learning; However, In The Literature There Is Still Very Limited Work To Address Active Learning For Ranking. In This Paper, We Propose A General Active Learning Framework, Expected Loss Optimization (ELO), For Ranking. The ELO Framework Is Applicable To A Wide Range Of Ranking Functions. Under This Framework, We Derive A Novel Algorithm, Expected Discounted Cumulative Gain (DCG) Loss Optimization (ELO-DCG), To Select Most Informative Examples. Then, We Investigate Both Query And Document Level Active Learning For Raking And Propose A Two-stage ELO-DCG Algorithm Which Incorporate Both Query And Document Selection Into Active Learning. Furthermore, We Show That It Is Flexible For The Algorithm To Deal With The Skewed Grade Distribution Problem With The Modification Of The Loss Function. Extensive Experiments On Real-world Web Search Data Sets Have Demonstrated Great Potential And Effectiveness Of The Proposed Framework And Algorithms.

REPRESENTATIVE PATTERN SETS- A Flexible Approach To Finding Representative Pattern Sets

Frequent Pattern Mining Often Produces An Enormous Number Of Frequent Patterns, Which Imposes A Great Challenge On Visualizing, Understanding And Further Analysis Of The Generated Patterns. This Calls For Finding A Small Number Of Representative Patterns To Best Approximate All Other Patterns. In This Paper, We Develop An Algorithm Called MinRPset To Find A Minimum Representative Pattern Set With Error Guarantee. MinRPset Produces The Smallest Solution That We Can Possibly Have In Practice Under The Given Problem Setting, And It Takes A Reasonable Amount Of Time To Finish When The Number Of Frequent Closed Patterns Is Below One Million. MinRPset Is Very Space-consuming And Time-consuming On Some Dense Datasets When The Number Of Frequent Closed Patterns Is Large. To Solve This Problem, We Propose Another Algorithm Called FlexRPset, Which Provides One Extra Parameter K To Allow Users To Make A Trade-off Between Result Size And Efficiency. We Adopt An Incremental Approach To Let The Users Make The Trade-off Conveniently. Our Experiment Results Show That MinRPset And FlexRPset Produce Fewer Representative Patterns Than RPlocal-an Efficient Algorithm That Is Developed For Solving The Same Problem.

IMAGE COMPRESSION ALGORITHM USING BINARY SPACE PARTITION SCHEME- Hierarchical Representation Of Plain Areas Of Post-Interpolation Residuals For Image Compression

We Propose An Algorithm For Encoding Quantized Post-interpolation Residuals Within The Framework Of Hierarchical Image Compression. This Coding Algorithm Is Based On A Hierarchical Representation Of The Plain Areas Of Quantized Post-interpolation Residuals To Improve The Coding Efficiency Of These Areas. The Proposed Algorithm Reorders The Post-interpolation Residuals To Increase The Size Of The Plain Areas. We Embed The Proposed Coding Algorithm For Post-interpolation Residuals Into A Hierarchical Image Compression Method. This Method Is Based On Interpolation The Image Scale Levels Using More Resampled Scale Levels Of The Same Image. The Errors Of This Interpolation (post-interpolation Residuals) Are Then Quantized And Encoded. We Use The Proposed Algorithm To Encode The Quantized Post-interpolation Residuals Of The Hierarchical Compression Method. We Perform Computational Experiments To Study The Effectiveness Of The Proposed Algorithm For A Set Of Natural Images. We Experimentally Confirm That The Use Of The Proposed Coding Algorithm For Post-interpolation Residuals Makes It Possible To Increase The Efficiency Of The Hierarchical Method Of Image Compression.

EFFICIENT JOINT ENCRYPTION AND DATA HIDING- A Security Technique For Authentication And Security Of Medical Images In Health Information Systems

In This Paper An Efficient Crypto-watermarking Algorithm Is Proposed To Secure Medical Images Transmitted In Tele-medicine Applications. The Proposed Algorithm Uses Standard Encryption Methods And Reversible Watermarking Techniques To Provide Security To The Transmitted Medical Images As Well As To Control Access Privileges At The Receiver Side. The Algorithm Jointly Embeds Two Watermarks In Two Domains Using Encryption And Reversible Watermarking To Avoid Any Interference Between The Watermarks. The Authenticity And Integrity Of Medical Images Can Be Verified In The Spatial Domain, The Encrypted Domain, Or In Both Domains. The Performance Of The Proposed Algorithm Is Evaluated Using Test Medical Images Of Different Modalities. The Algorithm Preforms Well In Terms Of Visual Quality Of The Watermarked Images And In Terms Of The Available Embedding Capacity.

EFFICIENT JOINT ENCRYPTION AND DATA HIDING- A Security Technique For Authentication And Security Of Medical Images In Health Information Systems

Medical Images Stored In Health Information Systems, Cloud Or Other Systems Are Of Key Importance. Privacy And Security Needs To Be Guaranteed For Such Images Through Encryption And Authentication Processes. Encrypted And Watermarked Images In This Domain Needed To Be Reversible So That The Plain Image Operated On In The Encryption And Watermarking Process Can Be Fully Recoverable Due To The Sensitivity Of The Data Conveyed In Medical Images. In This Paper, We Proposed A Fully Recoverable Encrypted And Watermarked Image Processing Technique For The Security Of Medical Images In Health Information Systems. The Approach Is Used To Authenticate And Secure The Medical Images. Our Results Showed To Be Very Effective And Reliable For Fully Recoverable Images.

REAL-TIME AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM- Real-time Automatic License Plate Recognition System Using YOLOv4

We Introduce A Real-time Automatic License Plate Recognition System That Is Computationally Lighter By Eliminating The ROI Setting Step, Without Deteriorating Recognition Performance. Conventional License Plate Recognition Systems Exhibit Two Main Problems. First, Clear License Plate Visibility Is Required. Second, Processing Actual Field Data Is Computationally Intensive And The ROI Needs To Be Set. To Overcome These Problems, We Performed Plate Localization Directly On The Entire Image, And Conducted Research Taking Low Quality License Plate Detection Into Account. We Aim To Recognize The License Plates Of Cars Moving At High Speeds On The Road As Well As Stationary Cars Using The NVIDIA Jetson TX2 Module, Which Is An Embedded Computing Device.

- Reversible Data Hiding In Encrypted Images Based On Reversible Integer Transformation And Quadtree-Based Partition

This Paper Presents An Improved Secure Reversible Data Hiding Scheme In Encrypted Images Based On Integer Transformation, Which Does Not Need Using A Data Hider Key To Protect The Embedded Secret Data. We First Segment The Original Image Into Blocks Of Various Sizes Based On The Quadtree-based Image Partition. For Each Block, We Reserve M Least Significant Bits (LSBs) Of Each Pixel As Embedding Room Based On The Reversible Integer Transformation. In Order To Improve The Security Of The Image Encryption, We Pad The MLSBs Of Each Pixel Using The Corresponding (8-m) Most Significant Bits (MSBs) Information After The Transformation, Which Protects The Security Of The Encryption Key. Then, We Encrypt The Transformed Image With A Standard Stream Cipher. After The Image Encryption, The Data Hider Embeds The Secret Data In The MLSBs Of The Encrypted Images Through An Exclusive Or Operation. On The Receiving Side, The Receiver Can Extract The Secret Data After The Image Decryption And Recover The Original Image Without Loss Of Quality. The Security Analysis Shows That The Proposed Scheme Improves The Security Weakness Of The Scheme Directly Using Adaptive Integer Transformation. The Experimental Results Show That The Proposed Method Achieves A Higher Embedding Ratio Compared With Several Relevant Methods.

IMAGE RESTORATION USING SPACE-VARIANT GAUSSIAN SCALE - Exaggerated Learning For Clean-And-Sharp Image Restoration

Deep Learning Has Become A Methodology Of Choice For Image Restoration Tasks, Including Denoising, Super-resolution, Deblurring, Exposure Correction, Etc., Because Of Its Superiority To Traditional Methods In Reconstruction Quality. However, The Published Deep Learning Methods Still Have Not Solve The Old Dilemma Between Low Noise Level And Detail Sharpness. We Propose A New CNN Design Strategy, Called Exaggerated Deep Learning, To Reconcile Two Mutually Conflicting Objectives: Noise Free And Detail Sharpness. The Idea Is To Deliberately Overshoot For The Desired Attributes In The CNN Optimization Objective Function; The Cleanness Or Sharpness Is Overemphasized According To Different Semantic Contexts. The Exaggerated Learning Approach Is Experimented On The Restoration Tasks Of Super-resolution And Low Light Correction. Its Effectiveness And Advantages Have Been Empirically Affirmed.

DETERMINISTIC DATA DISTRIBUTION FOR EFFICIENT RECOVERY IN ERASURE-CODED STORAGE SYSTEMS

Due To Individual Unreliable Commodity Components, Failures Are Common In Large-scale Distributed Storage Systems. Erasure Codes Are Widely Deployed In Practical Storage Systems To Provide Fault Tolerance With Low Storage Overhead

PROBABILISTIC MISBEHAVIOR DETECTION SCHEME- A Privacy-Preserving Misbehavior Detection System In Vehicular Communication Networks

5 G Based Vehicular Communication Networks Support Various Traffic Safety And Infotainment Use Cases And Rely On The Periodic Exchange Of Information. However, These Messages Are Susceptible To Several Attacks Which Can Be Detected Using Misbehavior Detection Systems (MDS). MDS Utilizes Trust Score, Feedback Score And Other Evaluation Schemes To Identify Abnormal Behavior Of The Vehicles. However, The Trust And Feedback Scores Used In MDS May Violate The Location, Trajectory, Or Identity Privacy Of The Vehicle. In This Paper, We Propose A Privacy-preserving Misbehavior Detection System That Can Detect Or Identify Misbehavior Without Violating The Privacy Of The Vehicle. In The Proposed Method, Encrypted Weighted Feedbacks Sent From Vehicles Are Combined Using Additive Homomorphic Properties Without Violating The Privacy Of The Information. The Decryption Of The Aggregate Feedback Is Done Securely At The Trusted Authority Which Updates The Reputation Score Of The Vehicle According To The Decrypted Aggregate Feedback Score. We Have Also Performed Comprehensive Security Analysis And Have Shown The Correctness And Resilience Of The Proposed Schemes Against Various Attacks. In Addition, We Have Done Extensive Performance Analysis And Have Shown That The Computation Cost Of The Proposed Scheme Is Better Compared To The Existing Schemes.

OBSERVER-BASED EVENT-TRIGGERED APPROACH FOR STOCHASTIC NETWORKED CONTROL SYSTEMS UNDER DENIAL OF SERVICE ATTACKS

In This Article, We Investigate The Stability Analysis And Controller Synthesis Problems For A Class Of Stochastic Networked Control Systems Under Aperiodic Denial-of-service Jamming Attacks.

FAST DETECTION OF MOBILE REPLICA NODE ATTACK - Detect The Replica Node In Mobile Wireless Sensor Networks

Security Is The Main Issue In WSN Applications. One Of The Important Attacks In WSN Is Node Replication Attacks. The Adversary Can Capture The Genuine Nodes. After Capturing The Node, The Attacker Collects All The Information Like Keys And Identity. In The Existing Method, The Replica Node Is Detected By The Parameter's Mobility Speed, Node Id And Energy. The Parameters Used In The Existing System Is Not Able To Detect The Exact Replica Node. Speedily Detecting A Replicated Node Will Avoid The Misbehavior Activities Such As Collecting All The Credentials, Etc. The Proposed Approach (FEC) Will Overcome The Issues Of Existing System. It Detects The Replica Node With Speed Of The Sensor Node. The Detection Accuracy Is High.

A COLLABORATIVE SECURITY FRAMEWORK FOR SOFTWARE-DEFINED WIRELESS SENSOR NETWORKS

With The Advent Of 5G, Technologies Such As Software-Defined Networks (SDNs) And Network Function Virtualization (NFV) Have Been Developed To Facilitate Simple Programmable Control Of Wireless Sensor Networks (WSNs).

DECENTRALIZED QOS - AWARE CHECKPOINTING- Scalable And QoS-aware Resource Allocation To Heterogeneous Traffic Flows

Networks Of New Generations Are Increasingly Involved In Transporting Heterogeneous Flows. Indeed, In Addition To The Usual Data And Multimedia Traffic, The Internet Of Things (IoT) Smart Applications Are Creating New Traffic Types And Relationships Involving Billions Of Active Nodes Like Sensors And Actuators. This Traffic Raises A Problem Of Scale, Particularly For Resource Management And Decision-making Mechanisms. The Present Work Addresses For The First Time The Joint Problem Of Mapping Heterogeneous Flows From Multiple Users And Applications To Transport Blocks, And Then Packing These Blocks Into The Rectangular Grid Of Time–frequency Resources Within A Flexible 5G New Radio Frame. Our Solution Is Based On A Quality-of-service-based Classification Of Flows Followed By An Offline Construction Of Two Databases. The First One Enumerates All Possible Configurations Of Transport Blocks And The Second Enumerates All Possible Configurations Of Frames. Thus, The Sole Online Processing That Remains To Be Done Is To Find The Optimal Block Configurations That Satisfy A Given Request Vector. Hence, The Resolution Of This Complex Joint Mapping And Packing Problem Is Reduced To A Simple Resolution Of A Linear Problem, Which Consists In Finding The Best Configurations. A Thorough Numerical Study Shows That Our Configuration-based Solution Can Map, Within Few Tens Of Milliseconds, More Than 100 Flow Connections To Transport Blocks Incurring Only 3% Of Overallocation, And Then Pack These Blocks Into The Grid Leading To An Upper Bound On The Optimality Gap As Low As 2.8%.

DATA-DRIVEN INTRUSION DETECTION FOR INTELLIGENT INTERNET OF VEHICLES A DEEP CONVOLUTIONAL NEURAL NETWORK-BASED METHOD

As An Industrial Application Of Internet Of Things (IoT), Internet Of Vehicles Is One Of The Most Crucial Techniques For Intelligent Transportation System, Which Is A Basic Element Of Smart Cities.

BRINGING INTELLIGENCE TO SOFTWARE DEFINED NETWORKS MITIGATING DDOS ATTACKS

DNS, One Of The Most Critical Elements Of The Internet, Is Among These Protocols. It Is Vulnerable To DDoS Attacks Mainly Because All Exchanges In This Protocol Use User Datagram Protocol (UDP).

DEVELOPMENT OF A SMART CYBER PHYSICAL MANUFACTURING SYSTEM IN THE INDUSTRY CONTEXT

The Complexity And Dynamic Of The Manufacturing Environment Are Growing Due To The Changes Of Manufacturing Demand From Mass Production To Mass Customization That Require Variable Product Types, Small Lot Sizes, And A Short Lead-time To Market. Currently, The Automatic Manufacturing Systems Are Suitable For Mass Production

Adaptive Diffusion Of Sensitive Information In Online Social Networks

The Cascading Of Sensitive Information Such As Private Contents And Rumors Is A Severe Issue In Online Social Networks.

OPTIMAL MULTIPATH ROUTING - Network-Aware Multipathing In Software-Defined Data Center Networks

Data Center Networks Employ Parallel Paths To Perform Load Balancing. Existing Traffic Splitting Schemes Propose Weighted Traffic Distribution Across Multiple Paths Via A Centralized View. An SDN Controller Computes The Traffic Splitting Ratio Of A Flow Group Among All The Paths, And Implements The Ratio By Creating Multiple Rules In The Flow Table Of OpenFlow Switches. However, Since The Number Of Rules In TCAM-based Flow Table Is Limited, It Is Not Scalable To Implement The Ideal Splitting Ratio For Every Flow Group. Existing Solutions, WCMP And Niagara, Aim At Reducing The Maximum Oversubscription Of All Egress Ports And Reducing Traffic Imbalance, Respectively. However, The Transmission Time Of Flow Groups, Which Measures The Quality Of Cloud Services, Is Sub-optimal In Existing Solutions That Ignore Heterogeneous Network Bandwidth. We Propose And Implement NAMP, A Multipathing Scheme Considering The Network Heterogeneity, To Efficiently Optimize The Transmission Time Of Flow Groups. Experimental Results Show That NAMP Reduces The Transmission Time By Up To 45.4% Than Niagara, Up To 50% Than WCMP, And Up To 60% Than ECMP.

WIRELESS COMMUNICATIONS SYSTEMS SUPPORTING MULTIMEDIA SERVICES - On SDN-driven Network Optimization And QoS Aware Routing Using Multiple Paths

Software Defined Networking (SDN) Is A Driving Technology For Enabling The 5th Generation Of Mobile Communication (5G) Systems Offering Enhanced Network Management Features And Softwarization. This Paper Concentrates On Reducing The Operating Expenditure (OPEX) Costs While I) Increasing The Quality Of Service (QoS) By Leveraging The Benefits Of Queuing And Multi-path Forwarding In OpenFlow, Ii) Allowing An Operator With An SDN-enabled Network To Efficiently Allocate The Network Resources Considering Mobility, And Iii) Reducing Or Even Eliminating The Need For Over-provisioning. For Achieving These Objectives, A QoS Aware Network Configuration And Multipath Forwarding Approach Is Introduced That Efficiently Manages The Operation Of SDN Enabled Open Virtual Switches (OVSs). This Paper Proposes And Evaluates Three Solutions That Exploit The Strength Of QoS Aware Routing Using Multiple Paths. While The Two First Solutions Provide Optimal And Approximate Optimal Configurations, Respectively, Using Linear Integer Programming Optimization, The Third One Is A Heuristic That Uses Dijkstra Short-path Algorithm. The Obtained Results Demonstrate The Performance Of The Proposed Solutions In Terms Of OPEX And Execution Time.

PRIVACY-PRESERVING LOCATION PROOFS FOR MOBILE USERS - A Decentralized Location Privacy-Preserving Spatial Crowdsourcing For Internet Of Vehicles

With The Rapid Development Of Internet Of Vehicles (IoV), Vehicle-based Spatial Crowdsourcing (SC) Applications Have Been Proposed And Widely Applied To Various Fields. However, Location Privacy Leakage Is A Serious Issue In Spatial Crowdsourcing Because Workers Who Participate In A Crowdsourcing Task Are Required To Upload Their Driving Locations. In This Paper, We Propose A Decentralized Location Privacy-preserving SC For IoV, Which Allows Vehicle Users To Securely Participate In SC With Ensuring The Task's Location Policy Privacy And Providing Multi-level Privacy Preservation For Workers' Locations. Specifically, We Introduce Blockchain Technology Into SC, Which Can Eliminate The Control Of Vehicle User Data By SC-server. We Combine The Additively Homomorphic Encryption And Circle-based Location Verification To Ensure The Confidentiality Of Task's Location Policy. To Achieve Multi-level Privacy Preservation For Workers' Driving Locations, We Only Reveal A Grid Where Workers Are Located In. The Size Of The Grid Represents The Level Of Privacy Preservation. We Leverage The Order-preserving Encryption And Non-interactive Zero-knowledge Proof To Prevent Workers From Illegally Obtaining Rewards By Forging Their Driving Locations. The Security Analysis Results Show That Our Framework Can Satisfy The Above Requirements. In Addition, The Experiment Results Demonstrate That Our Framework Is Efficient And Feasible In Practice.

DDOS ATTACK - Ddos Attack Using Kpg-Mt Technique

The Transformation Of Traditional Energy Networks To Smart Grids Can Assist In Revolutionizing The Energy Industry In Terms Of Reliability, Performance And Manageability. However, Increased Connectivity Of Power Grid Assets For Bidirectional Communications Presents Severe Security Vulnerabilities. In This Letter, We Investigate Chi-square Detector And Cosine Similarity Matching Approaches For Attack Detection In Smart Grids Where Kalman Filter Estimation Is Used To Measure Any Deviation From Actual Measurements. The Cosine Similarity Matching Approach Is Found To Be Robust For Detecting False Data Injection Attacks As Well As Other Attacks In The Smart Grids. Once The Attack Is Detected, System Can Take Preventive Action And Alarm The Manager To Take Preventative Action To Limit The Risk. Numerical Results Obtained From Simulations Corroborate Our Theoretical Analysis.

SECRET KEY EXTRACTION IN REAL ENVIRONMENTS- Performance Improvement Based On Modified Lossless Quantization MLQ For Secret Key Generation Extracted From Received Signal Strength

In Symmetric Cryptography Systems Have Problems In The Distribution Of Secret Keys. The Two Users Who Will Communicate Require Sharing Keys Through The Public Channel. The Proposed Solution To Overcome These Problems Is To Utilize Information From The Physical Layer (e.g. RSS). Received Signal Strength (RSS) Is An Indicator For Measuring The Power Received By Wireless Devices. The Advantage Of Secret Key Extraction Using Physical Layer Information From A Wireless Channel Is That It Allows Both Devices Within The Transmission Range To Extract The Secret Key Together. In This Paper, We Propose A Secret Key Generation Scheme Adopted From An Existing Scheme With Modifications To Improve Performance. Our Proposed System Is Applied To Static And Dynamic Conditions To Test Performance. The Proposed Algorithm Is Able To Obtain A Reduction In KDR (Key Disagreement Rate) Up To 48.42% And An Increase In The KGR (Key Generation Rate) Up To 23.35% When Compared To The Existing Scheme. Our Proposed System Also Successfully Passed The Randomness Using The NIST Test With The Approximate Value Of Entropy Generated 0.80 In Static Conditions And 0.81 In Dynamic Conditions.

DATA-CENTRIC SENSOR NETWORKS PDCS - Data-Centric Node Selection For Machine-Type Communications With Lossy Links

The Demand For Efficient Data Dissemination/access Techniques To Find The Relevant Data From Within A Sensor Network Has Led To The Development Of Data-centric Sensor Networks (DCS), Where The Sensor Data As Contrast To Sensor Nodes Are Named Based On Attributes Such As Event Type Or Geographic Location. However, Saving Data Inside A Network Also Creates Security Problems Due To The Lack Of Tamper-resistance Of The Sensor Nodes And The Unattended Nature Of The Sensor Network. For Example, An Attacker May Simply Locate And Compromise The Node Storing The Event Of His Interest. To Address These Security Problems, We Present PDCS, A Privacy-enhanced DCS Network Which Offers Different Levels Of Data Privacy Based On Different Cryptographic Keys. In Addition, We Propose Several Query Optimization Techniques Based On Euclidean Steiner Tree And Keyed Bloom Filter To Minimize The Query Overhead While Providing Certain Query Privacy. Finally, Detailed Analysis And Simulations Show That The Keyed Bloom Filter Scheme Can Significantly Reduce The Message Overhead With The Same Level Of Query Delay And Maintain A Very High Level Of Query Privacy.

NEGATIVE PASSWORD- Authenticate Encrypted Negative Password

Secure Password Storage Is A Vital Aspect In Systems Based On Password Authentication, Which Is Still The Most Widely Used Authentication Technique, Despite Some Security Flaws. In This Paper, We Propose A Password Authentication Framework That Is Designed For Secure Password Storage And Could Be Easily Integrated Into Existing Authentication Systems. In Our Framework, First, The Received Plain Password From A Client Is Hashed Through A Cryptographic Hash Function (e.g., SHA-256). Then, The Hashed Password Is Converted Into A Negative Password. Finally, The Negative Password Is Encrypted Into An Encrypted Negative Password (ENP) Using A Symmetric-key Algorithm (e.g., AES), And Multi-iteration Encryption Could Be Employed To Further Improve Security. The Cryptographic Hash Function And Symmetric Encryption Make It Difficult To Crack Passwords From ENPs. Moreover, There Are Lots Of Corresponding ENPs For A Given Plain Password, Which Makes Precomputation Attacks (e.g., Lookup Table Attack And Rainbow Table Attack) Infeasible. The Algorithm Complexity Analyses And Comparisons Show That The ENP Could Resist Lookup Table Attack And Provide Stronger Password Protection Under Dictionary Attack. It Is Worth Mentioning That The ENP Does Not Introduce Extra Elements (e.g., Salt); Besides This, The ENP Could Still Resist Precomputation Attacks. Most Importantly, The ENP Is The First Password Protection Scheme That Combines The Cryptographic Hash Function, The Negative Password, And The Symmetric-key Algorithm, Without The Need For Additional Information Except The Plain Password.

MULTI-APPLICATION SHARING WIRELESS SENSOR NETWORKS - An Efficient Many-to-Many Wireless Big Data Delivery Scheme

Wireless Big Data Raises The Demands On The Networking Schemes To Support The Efficient Group Data Sharing Over Heterogeneous Wireless Technologies, Which Take Many-to-many Data Delivery As The Foundation. Information-centric Networking (ICN) Approach Is A Promising Networking Technology To Support Big Data Delivery, Which Has The Potential To Establish The Harmony Between Networking And Wireless Big Data Sharing. However, The Existing ICN Schemes Have Not Carefully Addressed The Many-to-many Communications. To Address This Issue, We Propose An Efficient And Secure Many-to-many Wireless Big Data Delivery Scheme (MWBS) To Provide Group-based Data Dissemination And Retrieval With Name-integrated Forwarding. In MWBS, A Bi-directional Tree Is Securely Constructed For Each Group Through The Procedures Of Group Initiation, Join, Leave, Publication, And Multi-level Inter-zone Routing. Especially, Designated Forwarding And Cacheable Nodes (DFCNs) Are Introduced To Act As The Roots For The Construction Of Such Bi-directional Trees. The Implementation Details Of MWBS Are Provided For Function Verifications. To Effectively Deploy MWBS, We Investigate The Impacts To The MWBS Performance From The Number And Locations Of DFCNs, Which Show That The Optimized Number Of DFCNs Can Reduce The Total Traffic Cost And The DFCN Close To Users Is Preferred To Be Selected For A Group. Finally, Simulations Are Performed To Evaluate The Performance Of MWBS, Which Show That MWBS Can Reduce The Control Packet Overhead And The State Storage Overhead Compared To The Existing ICN Schemes.

TRUST MANAGEMENT USING UNCERTAIN REASONING- A Novel Trust Management Mechanism For Mobile Ad Hoc Networks Based On Grey Theory

Trust Management Mechanism Is A Hot Spot In The Research Of Mobile Ad Hoc Network Security. In View Of The Many Problems Of Trust Management Mechanism In Mobile Hoc Ad Networks, Combining With The Characteristics Of Mobile Ad Hoc Network, We Present A Mobile Ad Hoc Network Trust Management Mechanism Based On Grey Theory In This Paper And Apply It To The Mobile Ad Hoc Network Management In Order To Improve The Availability And Effectiveness Of Trust Management Mechanism And Safeguard The Security Of Mobile Ad Hoc Networks.

GROUP KEY AUTHENTICATION - An Authenticated Group Key Transfer Protocol Based On Secret Sharing

Key Transfer Protocols Rely On A Mutually Trusted Key Generation Center (KGC) To Select Session Keys And Transport Session Keys To All Communication Entities Secretly. Most Often, KGC Encrypts Session Keys Under Another Secret Key Shared With Each Entity During Registration. In This Paper, We Propose An Authenticated Key Transfer Protocol Based On Secret Sharing Scheme That KGC Can Broadcast Group Key Information To All Group Members At Once And Only Authorized Group Members Can Recover The Group Key; But Unauthorized Users Cannot Recover The Group Key. The Confidentiality Of This Transformation Is Information Theoretically Secure. We Also Provide Authentication For Transporting This Group Key. Goals And Security Threats Of Our Proposed Group Key Transfer Protocol Will Be Analyzed In Detail.

COGNITIVE SENSOR NETWORK - Spectrum Availability Based Routing For Cognitive Sensor Networks

With The Occurrence Of Internet Of Things (IoT) Era, The Proliferation Of Sensors Coupled With The Increasing Usage Of Wireless Spectrums Especially The ISM Band Makes It Difficult To Deploy Real-life IoT. Currently, The Cognitive Radio Technology Enables Sensors Transmit Data Packets Over The Licensed Spectrum Bands As Well As The Free ISM Bands. The Dynamic Spectrum Access Technology Enables Secondary Users (SUs) Access Wireless Channel Bands That Are Originally Licensed To Primary Users. Due To The High Dynamic Of Spectrum Availability, It Is Challenging To Design An Efficient Routing Approach For SUs In Cognitive Sensor Networks. We Estimate The Spectrum Availability And Spectrum Quality From The View Of Both The Global Statistical Spectrum Usage And The Local Instant Spectrum Status, And Then Introduce Novel Routing Metrics To Consider The Estimation. In Our Novel Routing Metrics, One Retransmission Is Allowed To Restrict The Number Of Rerouting And Then Increase The Routing Performance. Then, The Related Two Routing Algorithms According To The Proposed Routing Metrics Are Designed. Finally, Our Routing Algorithms In Extensive Simulations Are Implemented To Evaluate The Routing Performance, And We Find That The Proposed Algorithms Achieve A Significant Performance Improvement Compared With The Reference Algorithm.

TRUST-BASED PACKET FILTERING -Towards Effective Trust-Based Packet Filtering In Collaborative Networks Environments

Overhead Network Packets Are A Big Challenge For Intrusion Detection Systems (IDSs), Which May Increase System Burden, Degrade System Performance, And Even Cause The Whole System Collapse, When The Number Of Incoming Packets Exceeds The Maximum Handling Capability. To Address This Issue, Packet Filtration Is Considered As A Promising Solution, And Our Previous Research Efforts Have Proven That Designing A Trust-based Packet Filter Was Able To Refine Unwanted Network Packets And Reduce The Workload Of A Local IDS. With The Development Of Internet Cooperation, Collaborative Intrusion Detection Environments (e.g., CIDNs) Have Been Developed, Which Allow IDS Nodes To Collect Information And Learn Experience From Others. However, It Would Not Be Effective For The Previously Built Trust-based Packet Filter To Work In Such A Collaborative Environment, Since The Process Of Trust Computation Can Be Easily Compromised By Insider Attacks. In This Paper, We Adopt The Existing CIDN Framework And Aim To Apply A Collaborative Trust-based Approach To Reduce Unwanted Packets. More Specifically, We Develop A Collaborative Trust-based Packet Filter, Which Can Be Deployed In Collaborative Networks And Be Robust Against Typical Insider Attacks (e.g., Betrayal Attacks). Experimental Results In Various Simulated And Practical Environments Demonstrate That Our Filter Can Perform Effectively In Reducing Unwanted Traffic And Can Defend Against Insider Attacks Through Identifying Malicious Nodes In A Quick Manner, As Compared To Similar Approaches.

ANOMLY DETECTION - Anomaly Detection And Attribution In Networks With Temporally Correlated Traffic

Anomaly Detection In Communication Networks Is The First Step In The Challenging Task Of Securing A Network, As Anomalies May Indicate Suspicious Behaviors, Attacks, Network Malfunctions, Or Failures. In This Paper, We Address The Problem Of Not Only Detecting The Anomalous Events But Also Of Attributing The Anomaly To The Flows Causing It. To This End, We Develop A New Statistical Decision Theoretic Framework For Temporally Correlated Traffic In Networks Via Markov Chain Modeling. We First Formulate The Optimal Anomaly Detection Problem Via The Generalized Likelihood Ratio Test (GLRT) For Our Composite Model. This Results In A Combinatorial Optimization Problem Which Is Prohibitively Expensive. We Then Develop Two Low-complexity Anomaly Detection Algorithms. The First Is Based On The Cross Entropy (CE) Method, Which Detects Anomalies As Well As Attributes Anomalies To Flows. The Second Algorithm Performs Anomaly Detection Via GLRT On The Aggregated Flows Transformation - A Compact Low-dimensional Representation Of The Raw Traffic Flows. The Two Algorithms Complement Each Other And Allow The Network Operator To First Activate The Flow Aggregation Algorithm In Order To Quickly Detect Anomalies In The System. Once An Anomaly Has Been Detected, The Operator Can Further Investigate Which Specific Flows Are Anomalous By Running The CE-based Algorithm. We Perform Extensive Performance Evaluations And Experiment Our Algorithms On Synthetic And Semi-synthetic Data, As Well As On Real Internet Traffic Data Obtained From The MAWI Archive, And Finally Make Recommendations Regarding Their Usability.

PROTECT PERSONAL DATA - Decentralized Privacy Using Block Chain To Protect Personal Data

The Recent Increase In Reported Incidents Of Surveillance And Security Breaches Compromising Users' Privacy Call Into Question The Current Model, In Which Third-parties Collect And Control Massive Amounts Of Personal Data. Bit Coin Has Demonstrated In The Financial Space That Trusted, Auditable Computing Is Possible Using A Decentralized Network Of Peers Accompanied By A Public Ledger. In This Paper, We Describe A Decentralized Personal Data Management System That Ensures Users Own And Control Their Data. We Implement A Protocol That Turns A Block Chain Into An Automated Access-control Manager That Does Not Require Trust In A Third Party. Unlike Bit Coin, Transactions In Our System Are Not Strictly Financial -- They Are Used To Carry Instructions, Such As Storing, Querying And Sharing Data. Finally, We Discuss Possible Future Extensions To Block Chains That Could Harness Them Into A Well-rounded Solution For Trusted Computing Problems In Society.

DATA AGGREGATION OF COLLUSION ATTACKS- Secure Data Aggregation In Wireless Sensor Networks In The Presence Of Collusion Attacks

Due To Limited Computational Power And Energy Resources, Aggregation Of Data From Multiple Sensor Nodes Done At The Aggregating Node Is Usually Accomplished By Simple Methods Such As Averaging. However Such Aggregation Is Known To Be Highly Vulnerable To Node Compromising Attacks. Since WSN Are Usually Unattended And Without Tamper Resistant Hardware, They Are Highly Susceptible To Such Attacks. Thus, Ascertaining Trustworthiness Of Data And Reputation Of Sensor Nodes Is Crucial For WSN. As The Performance Of Very Low Power Processors Dramatically Improves, Future Aggregator Nodes Will Be Capable Of Performing More Sophisticated Data Aggregation Algorithms, Thus Making WSN Less Vulnerable. Iterative Filtering Algorithms Hold Great Promise For Such A Purpose. Such Algorithms Simultaneously Aggregate Data From Multiple Sources And Provide Trust Assessment Of These Sources, Usually In A Form Of Corresponding Weight Factors Assigned To Data Provided By Each Source. In This Paper We Demonstrate That Several Existing Iterative Filtering Algorithms, While Significantly More Robust Against Collusion Attacks Than The Simple Averaging Methods, Are Nevertheless Susceptive To A Novel Sophisticated Collusion Attack We Introduce. To Address This Security Issue, We Propose An Improvement For Iterative Filtering Techniques By Providing An Initial Approximation For Such Algorithms Which Makes Them Not Only Collusion Robust, But Also More Accurate And Faster Converging.

MESSAGE AUTHENTICATION AND SOURCE - Hop-by-Hop Message Authentication And Source Privacy In Wireless Sensor Networks

Message Authentication Is One Of The Most Effective Ways To Thwart Unauthorized And Corrupted Messages From Being Forwarded In Wireless Sensor Networks (WSNs). For This Reason, Many Message Authentication Schemes Have Been Developed, Based On Either Symmetric-key Cryptosystems Or Public-key Cryptosystems. Most Of Them, However, Have The Limitations Of High Computational And Communication Overhead In Addition To Lack Of Scalability And Resilience To Node Compromise Attacks. To Address These Issues, A Polynomial-based Scheme Was Recently Introduced. However, This Scheme And Its Extensions All Have The Weakness Of A Built-in Threshold Determined By The Degree Of The Polynomial: When The Number Of Messages Transmitted Is Larger Than This Threshold, The Adversary Can Fully Recover The Polynomial. In This Paper, We Propose A Scalable Authentication Scheme Based On Elliptic Curve Cryptography (ECC). While Enabling Intermediate Nodes Authentication, Our Proposed Scheme Allows Any Node To Transmit An Unlimited Number Of Messages Without Suffering The Threshold Problem. In Addition, Our Scheme Can Also Provide Message Source Privacy. Both Theoretical Analysis And Simulation Results Demonstrate That Our Proposed Scheme Is More Efficient Than The Polynomial-based Approach In Terms Of Computational And Communication Overhead Under Comparable Security Levels While Providing Message Source Privacy.

MISBEHAVIOUR DETECTION SCHEME - A Probabilistic Misbehavior Detection Scheme Toward Efficient Trust Establishment In Delay-Tolerant Networks

Malicious And Selfish Behaviors Represent A Serious Threat Against Routing In Delay/disruption Tolerant Networks (DTNs). Due To The Unique Network Characteristics, Designing A Misbehavior Detection Scheme In DTN Is Regarded As A Great Challenge. In This Paper, We Propose ITrust, A Probabilistic Misbehavior Detection Scheme, For Secure DTN Routing Toward Efficient Trust Establishment. The Basic Idea Of ITrust Is Introducing A Periodically Available Trusted Authority (TA) To Judge The Node's Behavior Based On The Collected Routing Evidences And Probabilistically Checking. We Model ITrust As The Inspection Game And Use Game Theoretical Analysis To Demonstrate That, By Setting An Appropriate Investigation Probability, TA Could Ensure The Security Of DTN Routing At A Reduced Cost. To Further Improve The Efficiency Of The Proposed Scheme, We Correlate Detection Probability With A Node's Reputation, Which Allows A Dynamic Detection Probability Determined By The Trust Of The Users. The Extensive Analysis And Simulation Results Demonstrate The Effectiveness And Efficiency Of The Proposed Scheme.

KEY EXCHAGE OVER INTERNET - Privacy-Preserving Authenticated Key-Exchange Over Internet

Key-exchange, In Particular Diffie-Hellman Key-exchange (DHKE), Is Among The Core Cryptographic Mechanisms For Ensuring Network Security. For Key-exchange Over The Internet, Both Security And Privacy Are Desired. In This Paper, We Develop A Family Of Privacy-preserving Authenticated DHKE Protocols Named Deniable Internet Key-exchange (DIKE), Both In The Traditional PKI Setting And In The Identity-based Setting. The Newly Developed DIKE Protocols Are Of Conceptual Clarity And Practical (online) Efficiency. They Provide Useful Privacy Protection To Both Protocol Participants, And Add Novelty And New Value To The IKE Standard. To The Best Of Our Knowledge, Our Protocols Are The First Provably Secure DHKE Protocols That Additionally Enjoy All The Following Privacy Protection Advantages: 1) Forward Deniability, Actually Concurrent Non-malleable Statistical Zero-knowledge, For Both Protocol Participants Simultaneously; 2) The Session Transcript And Session-key Can Be Generated Merely From DH-exponents (together With Some Public Values), Which Thus Cannot Be Traced To The Pair Of Protocol Participants; And 3) Exchanged Messages Do Not Bear Peer's Identity, And Do Not Explicitly Bear Player Role Information.

TRAFFICE ANALYSIS - Click Traffic Analysis Of Short URL Spam On Twitter

With An Average Of 80% Length Reduction, The URL Shorteners Have Become The Norm For Sharing URLs On Twitter, Mainly Due To The 140-character Limit Per Message. Unfortunately, Spammers Have Also Adopted The URL Shorteners To Camouflage And Improve The User Click-through Of Their Spam URLs. In This Paper, We Measure The Misuse Of The Short URLs And Analyze The Characteristics Of The Spam And Non-spam Short URLs. We Utilize These Measurements To Enable The Detection Of Spam Short URLs. To Achieve This, We Collected Short URLs From Twitter And Retrieved Their Click Traffic Data From Bitly, A Popular URL Shortening System. We First Investigate The Creators Of Over 600,000 Bitly Short URLs To Characterize Short URL Spammers. We Then Analyze The Click Traffic Generated From Various Countries And Referrers, And Determine The Top Click Sources For Spam And Non-spam Short URLs. Our Results Show That The Majority Of The Clicks Are From Direct Sources And That The Spammers Utilize Popular Websites To Attract More Attention By Cross-posting The Links. We Then Use The Click Traffic Data To Classify The Short URLs Into Spam Vs. Non-spam And Compare The Performance Of The Selected Classifiers On The Dataset. We Determine That The Random Tree Algorithm Achieves The Best Performance With An Accuracy Of 90.81% And An F-measure Value Of 0.913.

SHORTEST DISTANCE- Approximate Shortest Distance Computing-a Query Dependent Local Landmark Scheme

Shortest Distance Query Between Two Nodes Is A Fundamental Operation In Large-scale Networks. Most Existing Methods In The Literature Take A Landmark Embedding Approach, Which Selects A Set Of Graph Nodes As Landmarks And Computes The Shortest Distances From Each Landmark To All Nodes As An Embedding. To Handle A Shortest Distance Query Between Two Nodes, The Precomputed Distances From The Landmarks To The Query Nodes Are Used To Compute An Approximate Shortest Distance Based On The Triangle Inequality. In This Paper, We Analyze The Factors That Affect The Accuracy Of The Distance Estimation In The Landmark Embedding Approach. In Particular We Find That A Globally Selected, Query-independent Landmark Set Plus The Triangulation Based Distance Estimation Introduces A Large Relative Error, Especially For Nearby Query Nodes. To Address This Issue, We Propose A Query-dependent Local Landmark Scheme, Which Identifies A Local Landmark Close To The Specific Query Nodes And Provides A More Accurate Distance Estimation Than The Traditional Global Landmark Approach. Specifically, A Local Landmark Is Defined As The Least Common Ancestor Of The Two Query Nodes In The Shortest Path Tree Rooted At A Global Landmark. We Propose Efficient Local Landmark Indexing And Retrieval Techniques, Which Are Crucial To Achieve Low Offline Indexing Complexity And Online Query Complexity. Two Optimization Techniques On Graph Compression And Graph Online Search Are Also Proposed, With The Goal To Further Reduce Index Size And Improve Query Accuracy. Our Experimental Results On Large-scale Social Networks And Road Networks Demonstrate That The Local Landmark Scheme Reduces The Shortest Distance Estimation Error Significantly When Compared With Global Landmark Embedding.

SOCIAL STREAM - Detection Of Emerging Topics In Social Stream Using Link Anomaly

Detection Of Emerging Topics Is Now Receiving Renewed Interest Motivated By The Rapid Growth Of Social Networks. Conventional-term-frequency-based Approaches May Not Be Appropriate In This Context, Because The Information Exchanged In Social-network Posts Include Not Only Text But Also Images, URLs, And Videos. We Focus On Emergence Of Topics Signaled By Social Aspects Of Theses Networks. Specifically, We Focus On Mentions Of Users--links Between Users That Are Generated Dynamically (intentionally Or Unintentionally) Through Replies, Mentions, And Retweets. We Propose A Probability Model Of The Mentioning Behavior Of A Social Network User, And Propose To Detect The Emergence Of A New Topic From The Anomalies Measured Through The Model. Aggregating Anomaly Scores From Hundreds Of Users, We Show That We Can Detect Emerging Topics Only Based On The Reply/mention Relationships In Social-network Posts. We Demonstrate Our Technique In Several Real Data Sets We Gathered From Twitter. The Experiments Show That The Proposed Mention-anomaly-based Approaches Can Detect New Topics At Least As Early As Text-anomaly-based Approaches, And In Some Cases Much Earlier When The Topic Is Poorly Identified By The Textual Contents In Posts.

SOCIAL NETWORK - Experimental Analysis On Access Control Using Trust Parameter For Social Network

Technology Made Socializing Very Simple And Easy, Connecting Everyone Is Just A Matter Of A Click Today. The Security Of Our Personal Information And Sharing That Information In The Digital World Has Always Been A Major Challenge For The Ever-growing Social Networks. When It Comes To The Relationship Between People And Technology, The Attribution Of Trust Is A Matter Of Dispute Always. This Paper Proposes An Access Control Scheme Called Trust Based Access Control For Social Networks, Or STBAC, Which Allows Users To Share Data Among Their Friends, Using A Trust Computation To Determine Which Friends Should Be Given Access. This Trust Computation Uses Previous Interactions Among A User's Friends To Classify His Or Her Peers Into Privileged Or Unprivileged Zones, Which Determine Whether That Peer Gains Access To The User's Data. The System Will Work As A Filter For Each Of The Peer And Try To Evaluate The Trust Access Control In Social Networks.

TOLERANT NETWORK ROUTING- A Novel Message Scheduling Framework For Delay Tolerant Networks Routing

Multicopy Routing Strategies Have Been Considered The Most Applicable Approaches To Achieve Message Delivery In Delay Tolerant Networks (DTNs). Epidemic Routing And Two-hop Forwarding Routing Are Two Well-reported Approaches For Delay Tolerant Networks Routing Which Allow Multiple Message Replicas To Be Launched In Order To Increase Message Delivery Ratio And/or Reduce Message Delivery Delay. This Advantage, Nonetheless, Is At The Expense Of Additional Buffer Space And Bandwidth Overhead. Thus, To Achieve Efficient Utilization Of Network Resources, It Is Important To Come Up With An Effective Message Scheduling Strategy To Determine Which Messages Should Be Forwarded And Which Should Be Dropped In Case Of Buffer Is Full. This Paper Investigates A New Message Scheduling Framework For Epidemic And Two-hop Forwarding Routing In DTNs, Such That The Forwarding/dropping Decision Can Be Made At A Node During Each Contact For Either Optimal Message Delivery Ratio Or Message Delivery Delay. Extensive Simulation Results Show That The Proposed Message Scheduling Framework Can Achieve Better Performance Than Its Counterparts.

INTERNET DISTRIBUTED SYSTEM - Optimal Client Server Assignment For Internet Distributed Systems

We Investigate An Underlying Mathematical Model And Algorithms For Optimizing The Performance Of A Class Of Distributed Systems Over The Internet. Such A System Consists Of A Large Number Of Clients Who Communicate With Each Other Indirectly Via A Number Of Intermediate Servers. Optimizing The Overall Performance Of Such A System Then Can Be Formulated As A Client-server Assignment Problem Whose Aim Is To Assign The Clients To The Servers In Such A Way To Satisfy Some Prespecified Requirements On The Communication Cost And Load Balancing. We Show That 1) The Total Communication Load And Load Balancing Are Two Opposing Metrics, And Consequently, Their Tradeoff Is Inherent In This Class Of Distributed Systems; 2) In General, Finding The Optimal Client-server Assignment For Some Prespecified Requirements On The Total Load And Load Balancing Is NP-hard, And Therefore; 3) We Propose A Heuristic Via Relaxed Convex Optimization For Finding The Approximate Solution. Our Simulation Results Indicate That The Proposed Algorithm Produces Superior Performance Than Other Heuristics, Including The Popular Normalized Cuts Algorithm.

IDENTITY BASED CYPTOGRAPHY - A Distributed Private-key Generator For Identity-Based Cryptography

An Identity-based Encryption (IBE) Scheme Can Greatly Reduce The Complexity Of Sending Encrypted Messages. However, An IBE Scheme Necessarily Requires A Private-key Generator (PKG), Which Can Create Private Keys For Clients, And So Can Passively Eavesdrop On All Encrypted Communications. Although A Distributed PKG Has Been Suggested As A Way To Mitigate This Key Escrow Problem For Boneh And Franklin’s IBE Scheme, The Security Of This Distributed Protocol Has Not Been Proven. Further, A Distributed PKG Has Not Been Considered For Any Other IBE Scheme. In This Paper, We Design Distributed PKG Setup And Private Key Extraction Protocols For Three Important IBE Schemes; Namely, Boneh And Franklin’s BF-IBE, Sakai And Kasahara’s SK-IBE, And Boneh And Boyen’s BB1 -IBE. We Give Special Attention To The Applicability Of Our Protocols To All Possible Types Of Bilinear Pairings And Prove Their IND-ID-CCA Security In The Random Oracle Model Against A Byzantine Adversary. Finally, We Also Perform A Comparative Analysis Of These Protocols And Present Recommendations For Their Use.

CONTROLLING IP SPOOFING - Controlling IP Spoofing Through Inter-Domain Packet Filters

The Distributed Denial-of-service (DDoS) Attack Is A Serious Threat To The Legitimate Use Of The Internet. Prevention Mechanisms Are Thwarted By The Ability Of Attackers To Forge Or Spoof The Source Addresses In IP Packets. By Employing IP Spoofing, Attackers Can Evade Detection And Put A Substantial Burden On The Destination Network For Policing Attack Packets. In This Paper, We Propose An Interdomain Packet Filter (IDPF) Architecture That Can Mitigate The Level Of IP Spoofing On The Internet. A Key Feature Of Our Scheme Is That It Does Not Require Global Routing Information. IDPFs Are Constructed From The Information Implicit In Border Gateway Protocol (BGP) Route Updates And Are Deployed In Network Border Routers. We Establish The Conditions Under Which The IDPF Framework Correctly Works In That It Does Not Discard Packets With Valid Source Addresses. Based On Extensive Simulation Studies, We Show That, Even With Partial Deployment On The Internet, IDPFs Can Proactively Limit The Spoofing Capability Of Attackers. In Addition, They Can Help Localize The Origin Of An Attack Packet To A Small Number Of Candidate Networks.

ESM- Efficient And Scalable Data Center Multicast Routing

Multicast Benefits Group Communications In Saving Network Traffic And Improving Application Throughput, Both Of Which Are Important For Data Center Applications. However, The Technical Trend Of Data Center Design Poses New Challenges For Efficient And Scalable Multicast Routing. First, The Densely Connected Networks Make Traditional Receiver-driven Multicast Routing Protocols Inefficient In Multicast Tree Formation. Second, It Is Quite Difficult For The Low-end Switches Widely Used In Data Centers To Hold The Routing Entries Of Massive Multicast Groups. In This Paper, We Propose ESM, An Efficient And Scalable Multicast Routing Scheme For Data Center Networks. ESM Addresses The Challenges Above By Exploiting The Feature Of Modern Data Center Networks. Based On The Regular Topology Of Data Centers, ESM Uses A Source-to-receiver Expansion Approach To Build Efficient Multicast Trees, Excluding Many Unnecessary Intermediate Switches Used In Receiver-driven Multicast Routing. For Scalable Multicast Routing, ESM Combines Both In-packet Bloom Filters And In-switch Entries To Make The Tradeoff Between The Number Of Multicast Groups Supported And The Additional Bandwidth Overhead. Simulations Show That ESM Saves 40% ~ 50% Network Traffic And Doubles The Application Throughputs Compared To Receiver-driven Multicast Routing, And The Combination Routing Scheme Significantly Reduces The Number Of In-switch Entries Required. We Implement ESM On A Linux Platform. The Experimental Results Further Demonstrate That ESM Can Well Support Online Tree Building For Large-scale Groups With Churns, And The Overhead Of The Combination Forwarding Engine Is Light-weighted.

COLLABORATIVE PROTECTION NETWORK - Firecol A Collaborative Protection Network For Vehicle Safety Communication On The Freeway

Distributed Denial-of-service (DDoS) Attacks Remain A Major Security Problem, The Mitigation Of Which Is Very Hard Especially When It Comes To Highly Distributed Botnet-based Attacks. The Early Discovery Of These Attacks, Although Challenging, Is Necessary To Protect End-users As Well As The Expensive Network Infrastructure Resources. In This Paper, We Address The Problem Of DDoS Attacks And Present The Theoretical Foundation, Architecture, And Algorithms Of FireCol. The Core Of FireCol Is Composed Of Intrusion Prevention Systems (IPSs) Located At The Internet Service Providers (ISPs) Level. The IPSs Form Virtual Protection Rings Around The Hosts To Defend And Collaborate By Exchanging Selected Traffic Information. The Evaluation Of FireCol Using Extensive Simulations And A Real Dataset Is Presented, Showing FireCol Effectiveness And Low Overhead, As Well As Its Support For Incremental Deployment In Real Networks.

DETECTION OF FALSE DATA INJECTION ATTACKS - Cyber Attack Detection Based On Wavelet Singular Entropy In AC Smart Islands False Data Injection Attack

Since Smart-Islands (SIs) With Advanced Cyber-infrastructure Are Incredibly Vulnerable To Cyber-attacks, Increasing Attention Needs To Be Applied To Their Cyber-security. False Data Injection Attacks (FDIAs) By Manipulating Measurements May Cause Wrong State Estimation (SE) Solutions Or Interfere With The Central Control System Performance. There Is A Possibility That Conventional Attack Detection Methods Do Not Detect Many Cyber-attacks; Hence, System Operation Can Interfere. Research Works Are More Focused On Detecting Cyber-attacks That Target DC-SE; However, Due To More Widely Uses Of AC SIs, Investigation On Cyber-attack Detection In AC Systems Is More Crucial. In These Regards, A New Mechanism To Detect Injection Of Any False Data In AC-SE Based On Signal Processing Technique Is Proposed In This Paper. Malicious Data Injection In The State Vectors May Cause Deviation Of Their Temporal And Spatial Data Correlations From Their Ordinary Operation. The Suggested Detection Method Is Based On Analyzing Temporally Consecutive System States Via Wavelet Singular Entropy (WSE). In This Method, To Adjust Singular Value Matrices And Wavelet Transforms' Detailed Coefficients, Switching Surface Based On Sliding Mode Controller Are Decomposed; Then, By Applying The Stochastic Process, Expected Entropy Values Are Calculated. Indices Are Characterized Based On The WSE In Switching Level Of Current And Voltage For Cyber-attack Detection. The Proposed Detection Method Is Applied To Different Case Studies To Detect Cyber-attacks With Various Types Of False Data Injection, Such As Amplitude, And Vector Deviation Signals. The Simulation Results Confirm The High-performance Capability Of The Proposed FDIA Detection Method. This Detection Method's Significant Characteristic Is Its Ability In Fast Detection (10 Ms From The Attack Initiation); Besides, This Technique Can Achieve An Accuracy Rate Of Over 96.5%.

SECURE DATA AGGREGATION IN THE PRESENCE OF COLLUSION ATTACKS- Trust Evaluation For Securing Compromised Data Aggregation Against The Collusion Attack In WSN

With A Storage Space Limit On The Sensors, WSN Has Some Drawbacks Related To Bandwidth And Computational Skills. This Limited Resources Would Reduce The Amount Of Data Transmitted Across The Network. For This Reason, Data Aggregation Is Considered As A New Process. Iterative Filtration (IF) Algorithms, Which Provide Trust Assessment To The Various Sources From Which The Data Aggregation Has Been Performed, Are Efficient In The Present Data Aggregation Algorithms. Trust Assessment Is Done With Weights From The Simple Average Method To Aggregation, Which Treats Attack Susceptibility. Iteration Filter Algorithms Are Stronger Than The Ordinary Average, But They Do Not Handle The Current Advanced Attack That Takes Advantage Of False Information With Many Compromise Nodes. Iterative Filters Are Strengthened By An Initial Confidence Estimate To Track New And Complex Attacks, Improving The Solidity And Accuracy Of The IF Algorithm. The New Method Is Mainly Concerned With Attacks Against The Clusters And Not Against The Aggregator. In This Process, If An Aggregator Is Attacked, The Current System Fails, And The Information Is Eventually Transmitted To The Aggregator By The Cluster Members. This Problem Can Be Detected When Both Cluster Members And Aggregators Are Being Targeted. It Is Proposed To Choose An Aggregator Which Chooses A New Aggregator According To The Remaining Maximum Energy And Distance To The Base Station When An Aggregator Attack Is Detected. It Also Save Time And Energy Compared To The Current Program Against The Corrupted Aggregator Node.

BOTNET FINGERPRINTING- A Frequency Distributions Scheme For Lightweight Bot Detection

Efficient Bot Detection Is A Crucial Security Matter And Widely Explored In The Past Years. Recent Approaches Supplant Flow-based Detection Techniques And Exploit Graph-based Features, Incurring However In Scalability Issues, With High Time And Space Complexity. Bots Exhibit Specific Communication Patterns: They Use Particular Protocols, Contact Specific Domains, Hence Can Be Identified By Analyzing Their Communication With The Outside. A Way We Follow To Simplify The Communication Graph And Avoid Scalability Issues Is Looking At Frequency Distributions Of Protocol Attributes Capturing The Specificity Of Botnets Behaviour. We Propose A Bot Detection Technique Named BotFP, For BotFingerPrinting, Which Acts By (i) Characterizing Hosts Behaviour With Attribute Frequency Distribution Signatures, (ii) Learning Benign Hosts And Bots Behaviours Through Either Clustering Or Supervised Machine Learning (ML), And (iii) Classifying New Hosts Either As Bots Or Benign Ones, Using Distances To Labelled Clusters Or Relying On A ML Algorithm. We Validate BotFP On The CTU-13 Dataset, Which Contains 13 Scenarios Of Bot Infections, Connecting To A Command-and-Control (C&C) Channel And Launching Malicious Actions Such As Port Scanning Or Denial-of-Service (DDoS) Attacks. Compared To State-of-the-art Techniques, We Show That BotFP Is More Lightweight, Can Handle Large Amounts Of Data, And Shows Better Accuracy.

ATTACKS ON PASSWORDS - One Time Password Guessing Attacks

A Fundamental Premise Of SMS One-Time Password (OTP) Is That The Used Pseudo-random Numbers (PRNs) Are Uniquely Unpredictable For Each Login Session. Hence, The Process Of Generating PRNs Is The Most Critical Step In The OTP Authentication. An Improper Implementation Of The Pseudorandom Number Generator (PRNG) Will Result In Predictable Or Even Static OTP Values, Making Them Vulnerable To Potential Attacks. In This Paper, We Present A Vulnerability Study Against PRNGs Implemented For Android Apps. A Key Challenge Is That PRNGs Are Typically Implemented On The Server-side, And Thus The Source Code Is Not Accessible. To Resolve This Issue, We Build An Analysis Tool, OTP-Lint, To Assess Implementations Of The PRNGs In An Automated Manner Without The Source Code Requirement. Through Reverse Engineering, OTP-Lint Identifies The Apps Using SMS OTP And Triggers Each App's Login Functionality To Retrieve OTP Values. It Further Assesses The Randomness Of The OTP Values To Identify Vulnerable PRNGs. By Analyzing 6,431 Commercially Used Android Apps Downloaded From Google Play And Tencent Myapp, OTP-Lint Identified 399 Vulnerable Apps That Generate Predictable OTP Values. Even Worse, 194 Vulnerable Apps Use The OTP Authentication Alone Without Any Additional Security Mechanisms, Leading To Insecure Authentication Against Guessing Attacks And Replay Attacks.

DETECTION OF MALICIOUS EMAIL- Malicious Mail Filtering And Tracing System Based On KNN And Improved LSTM Algorithm

Spam And Phishing Emails Are Very Troublesome Problems For Mailbox Users. Many Enterprises, Departments And Individuals Are Harmed By Them. Moreover, The Senders Of These Malicious Emails Are In A Hidden Position And Occupy An Initiative Position. The Existing Mailbox Services Can Only Filter And Shield Some Malicious Mails, Which Is Difficult To Reverse The Disadvantage Of Users. To Solve These Problems, We Propose A Secure Mail System Using K-nearest Neighbor(KNN) Algorithm And Improved Long Short-term Memory(LSTM) Algorithm(Bi-LSTM-Attention Algorithm). KNN Classifier Can Effectively Distinguish Normal Emails, Spam And Phishing Emails, And Has A High Accuracy. Bi-LSTM-Attention Classifier Classifies Phishing Emails According To The Similarity Of The Malicious Mail Text From The Same Attacker To Some Extent. By Classifying And Identifying The Source Of Malicious Emails, We Can Grasp The Characteristics Of The Attacker, Provide Materials For Further Research, And Improve The Passive Status Of Users. Experiments Show That The Classification Results Of Attack Sources Reach 90%, Which Indicate The Value Of Further Research And Promotion.

NICE-NETWORK INTRUSION DETECTION - Virtual Machine Introspection For Anomaly-Based Keylogger Detection

Today's Architectures For Intrusion Detection Force The IDS Designer To Make A Difficult Choice. If The IDS Resides On The Host, It Has An Excellent View Of What Is Happening In That Host's Software, But Is Highly Susceptible To Attack. On The Other Hand, If The IDS Resides In The Network, It Is More Resistant To Attack, But Has A Poor View Of What Is Happening Inside The Host, Making It More Susceptible To Evasion. In This Paper We Present An Architecture That Retains The Visibility Of A Host-based IDS, But Pulls The IDS Outside Of The Host For Greater Attack Resistance. We Achieve This Through The Use Of A Virtual Machine Monitor. Using This Approach Allows Us To Isolate The IDS From The Monitored Host But Still Retain Excellent Visibility Into The Host's State. The VMM Also Offers Us The Unique Ability To Completely Mediate Interactions Between The Host Software And The Underlying Hardware. We Present A Detailed Study Of Our Architecture, Including Livewire, A Prototype Implementation. We Demonstrate Livewire By Implementing A Suite Of Simple Intrusion Detection Policies And Using Them To Detect Real Attacks.

DISTRIBUTED INFORMATION SHARING Enforcing Secure And Privacy Preserving Information Brokering In Distributed Information Sharing

Today's Organizations Raise An Increasing Need For Information Sharing Via On-demand Access. Information Brokering Systems (IBSs) Have Been Proposed To Connect Large-scale Loosely Federated Data Sources Via A Brokering Overlay, In Which The Brokers Make Routing Decisions To Direct Client Queries To The Requested Data Servers. Many Existing IBSs Assume That Brokers Are Trusted And Thus Only Adopt Server-side Access Control For Data Confidentiality. However, Privacy Of Data Location And Data Consumer Can Still Be Inferred From Metadata (such As Query And Access Control Rules) Exchanged Within The IBS, But Little Attention Has Been Put On Its Protection. In This Paper, We Propose A Novel Approach To Preserve Privacy Of Multiple Stakeholders Involved In The Information Brokering Process. We Are Among The First To Formally Define Two Privacy Attacks, Namely Attribute-correlation Attack And Inference Attack, And Propose Two Countermeasure Schemes Automaton Segmentation And Query Segment Encryption To Securely Share The Routing Decision-making Responsibility Among A Selected Set Of Brokering Servers. With Comprehensive Security Analysis And Experimental Results, We Show That Our Approach Seamlessly Integrates Security Enforcement With Query Routing To Provide System-wide Security With Insignificant Overhead.

DEFENSE STRATEGY AGAINST FLOODING ATTACKS USING GAME THEORY - Defending Link Flooding Attacks Under Incomplete Information A Bayesian Game Approach

The Link flooding Attack (LFA) Arises As A New Classof Distributed Denial Of Service (DDoS) Attacks In Recent Years.By Aggregating Low-rate Protocol-conforming Traffic To Congestselected Links, LFAs Can Degrade Connectivity Or Saturate Targetservers Indirectly. Due To Fast Proliferation Of Insecure Internetof Things (IOT) Devices, The Deployment Of Botnets Is Gettingeasier, Which Dramatically Increases The Risk Of LFAs. Since Theattacking Traffic May Not Reach The Victims Directly And Is Usuallylegitimate, LFAs Are Extremely Difficult To Detect And Defendby Traditional Methods. In This Work, We Model The Interactionbetween LFA Attackers And Defenders As A Two-person Extensiveform Bayesian Game With Incomplete Information. By Using Actionabstraction And The Divide And Conquer Method, We Analyzethe Nash Equilibrium On Each Link, Which Reveals The Rationalbehavior Of Attackers And The Optimal Strategy Of Defenders.Furthermore, We Concretely Expound How To Adopt Local Optimalstrategies In The Internet-wide Scenario. Experimental Resultsshow The Effectiveness And Robustness Of Our Proposed Decision-making Method In Explicit LFA Defending Scenarios.

GROUP SECRET KEY GENERATION- Group Secret-Key Generation Using Algebraic Rings In Wireless Networks

It Is Well Known That Physical-layer Group Secret-Key (GSK) Generation Techniques Allow Multiple Nodes Of A Wireless Network To Synthesize A Common Secret-key, Which Can Be Subsequently Used To Keep Their Group Messages Confidential. As One Of Its Salient Features, The Wireless Nodes Involved In Physical-layer GSK Generation Extract Randomness From A Subset Of Their Wireless Channels, Referred As The Common Source Of Randomness (CSR). Unlike Two-user Key Generation, In GSK Generation, Some Nodes Must Act As Facilitators By Broadcasting Quantized Versions Of The Linear Combinations Of The Channel Realizations, So As To Assist All The Nodes To Observe A CSR. However, We Note That Broadcasting Linear Combination Of Channel Realizations Incurs Non-zero Leakage Of The CSR To An Eavesdropper, And Moreover, Quantizing The Linear Combination Also Reduces The Overall Key-rate. Identifying These Issues, We Propose A Practical GSK Generation Protocol, Referred To As Algebraic Symmetrically Quantized GSK (A-SQGSK) Protocol, In A Network Of Three Nodes, Wherein Due To Quantization Of Symbols At The Facilitator, The Other Two Nodes Also Quantize Their Channel Realizations, And Use Them Appropriately Over Algebraic Rings To Generate The Keys. First, We Prove That The A-SQGSK Protocol Incurs Zero Leakage To An Eavesdropper. Subsequently, On The CSR Provided By The A-SQGSK Protocol, We Propose A Consensus Algorithm Among The Three Nodes, Called The Entropy-Maximization Error-Minimization (EM-EM) Algorithm, Which Maximizes The Entropy Of The Secret-key Subject To An Upper-bound On The Mismatch-rate. We Use Extensive Analysis And Simulation Results To Lay Out Guidelines To Jointly Choose The Parameters Of The A-SQGSK Protocol And The EM-EM Algorithm.

VELNERABILITY ANALYSIS - An Integrated Approach For Effective Injection Vulnerability Analysis Of Web Applications Through Security Slicing And Hybrid Constraint

Malicious Users Can Attack Web Applications By Exploiting Injection Vulnerabilities In The Source Code. This Work Addresses The Challenge Of Detecting Injection Vulnerabilities In The Server-side Code Of Java Web Applications In A Scalable And Effective Way. We Propose An Integrated Approach That Seamlessly Combines Security Slicing With Hybrid Constraint Solving; The Latter Orchestrates Automata-based Solving With Meta-heuristic Search. We Use Static Analysis To Extract Minimal Program Slices Relevant To Security From Web Programs And To Generate Attack Conditions. We Then Apply Hybrid Constraint Solving To Determine The Satisfiability Of Attack Conditions And Thus Detect Vulnerabilities. The Experimental Results, Using A Benchmark Comprising A Set Of Diverse And Representative Web Applications/services As Well As Security Benchmark Applications, Show That Our Approach (implemented In The JOACO Tool) Is Significantly More Effective At Detecting Injection Vulnerabilities Than State-of-the-art Approaches, Achieving 98 Percent Recall, Without Producing Any False Alarm. We Also Compared The Constraint Solving Module Of Our Approach With State-of-the-art Constraint Solvers, Using Six Different Benchmark Suites; Our Approach Correctly Solved The Highest Number Of Constraints (665 Out Of 672), Without Producing Any Incorrect Result, And Was The One With The Least Number Of Time-out/failing Cases. In Both Scenarios, The Execution Time Was Practically Acceptable, Given The Offline Nature Of Vulnerability Detection.

JAMMING ATTACKS USING PACKET-HIDING METHODS- JIRS-based Wireless Jamming Attacks- When Jammers Can Attack Without Power

This Letter Proposes To Use Intelligent Reflecting Surface (IRS) As A Green Jammer To Attack A Legitimate Communication Without Using Any Internal Energy To Generate Jamming Signals. In Particular, The IRS Is Used To Intelligently Reflect The Signals From The Legitimate Transmitter To The Legitimate Receiver (LR) To Guarantee That The Received Signals From Direct And Reflecting Links Can Be Added Destructively, Which Thus Diminishes The Signal-to-Interference-plus-Noise Ratio (SINR) At The LR. To Minimize The Received Signal Power At The LR, We Consider The Joint Optimization Of Magnitudes Of Reflection Coefficients And Discrete Phase Shifts At The IRS. Based On The Block Coordinate Descent, Semidefinite Relaxation, And Gaussian Randomization Techniques, The Solution Can Be Obtained Efficiently. Through Simulation Results, We Show That By Using The IRS-based Jammer, We Can Reduce The Signal Power Received At The LR By Up To 99%. Interestingly, The Performance Of The Proposed IRS-based Jammer Is Even Better Than That Of The Conventional Active Jamming Attacks In Some Scenarios.

ONLINE PASSWORD GUSSING ATTACK - Revisiting Defence Against Large Scale Online Password Guessing Attacks

Brute Force And Dictionary Attacks On Password-only Remote Login Services Are Now Widespread And Ever Increasing. Enabling Convenient Login For Legitimate Users While Preventing Such Attacks Is A Difficult Problem. Automated Turing Tests (ATTs) Continue To Be An Effective, Easy-to-deploy Approach To Identify Automated Malicious Login Attempts With Reasonable Cost Of Inconvenience To Users. In This Paper, We Discuss The Inadequacy Of Existing And Proposed Login Protocols Designed To Address Large-scale Online Dictionary Attacks (e.g., From A Botnet Of Hundreds Of Thousands Of Nodes). We Propose A New Password Guessing Resistant Protocol (PGRP), Derived Upon Revisiting Prior Proposals Designed To Restrict Such Attacks. While PGRP Limits The Total Number Of Login Attempts From Unknown Remote Hosts To As Low As A Single Attempt Per Username, Legitimate Users In Most Cases (e.g., When Attempts Are Made From Known, Frequently-used Machines) Can Make Several Failed Login Attempts Before Being Challenged With An ATT. We Analyze The Performance Of PGRP With Two Real-world Data Sets And Find It More Promising Than Existing Proposals.

SECURITY LEAKAGE - Discovery And Resolution Of Anomalies In Web Access Control Policy

Emerging Computing Technologies Such As Web Services, Service-oriented Architecture, And Cloud Computing Has Enabled Us To Perform Business Services More Efficiently And Effectively. However, We Still Suffer From Unintended Security Leakages By Unauthorized Actions In Business Services While Providing More Convenient Services To Internet Users Through Such A Cutting-edge Technological Growth. Furthermore, Designing And Managing Web Access Control Policies Are Often Error-prone Due To The Lack Of Effective Analysis Mechanisms And Tools. In This Paper, We Represent An Innovative Policy Anomaly Analysis Approach For Web Access Control Policies, Focusing On Extensible Access Control Markup Language Policy. We Introduce A Policy-based Segmentation Technique To Accurately Identify Policy Anomalies And Derive Effective Anomaly Resolutions, Along With An Intuitive Visualization Representation Of Analysis Results. We Also Discuss A Proof-of-concept Implementation Of Our Method Called XAnalyzer And Demonstrate How Our Approach Can Efficiently Discover And Resolve Policy Anomalies.

MALICIOUS PEERS - SORT - A Self-Organizing Trust Model For Peer-to-Peer Systems

Open Nature Of Peer-to-peer Systems Exposes Them To Malicious Activity. Building Trust Relationships Among Peers Can Mitigate Attacks Of Malicious Peers. This Paper Presents Distributed Algorithms That Enable A Peer To Reason About Trustworthiness Of Other Peers Based On Past Interactions And Recommendations. Peers Create Their Own Trust Network In Their Proximity By Using Local Information Available And Do Not Try To Learn Global Trust Information. Two Contexts Of Trust, Service, And Recommendation Contexts, Are Defined To Measure Trustworthiness In Providing Services And Giving Recommendations. Interactions And Recommendations Are Evaluated Based On Importance, Recentness, And Peer Satisfaction Parameters. Additionally, Recommender's Trustworthiness And Confidence About A Recommendation Are Considered While Evaluating Recommendations. Simulation Experiments On A File Sharing Application Show That The Proposed Model Can Mitigate Attacks On 16 Different Malicious Behavior Models. In The Experiments, Good Peers Were Able To Form Trust Relationships In Their Proximity And Isolate Malicious Peers.

DENIAL OF SERVICE ATTACKS- Defending Against Denial Of Service Attacks

Due To Its Cost Efficiency The Controller Area Network (CAN) Is Still The Most Wide-spread In-vehicle Bus And The Numerous Reported Attacks Demonstrate The Urgency In Designing New Security Solutions For CAN. In This Work We Propose An Intrusion Detection Mechanism That Takes Advantage Of Bloom Filtering To Test Frame Periodicity Based On Message Identifiers And Parts Of The Data-field Which Facilitates Detection Of Potential Replay Or Modification Attacks. This Proves To Be An Effective Approach Since Most Of The Traffic From In-vehicle Buses Is Cyclic In Nature And The Format Of The Data-field Is Fixed Due To Rigid Signal Allocation. Bloom Filters Provide An Efficient Time-memory Tradeoff Which Is Beneficial For The Constrained Resources Of Automotive Grade Controllers. We Test The Correctness Of Our Approach And Obtain Good Results On An Industry-standard CANoe Based Simulation For A J1939 Commercial-vehicle Bus And Also On CAN-FD Traces Obtained From A Real-world High-end Vehicle. The Proposed Filtering Mechanism Is Straight-forward To Adapt For Any Other Time-triggered In-vehicle Bus, E.g., FlexRay, Since It Is Built On Time-driven Characteristics.

WIRELESS MESH NETWORKS - On The Security Of A Ticket Based Anonymity System With Traceability Property In Wireless Mesh Networks

In 2011, Sun Et Al. Proposed A Security Architecture To Ensure Unconditional Anonymity For Honest Users And Traceability Of Misbehaving Users For Network Authorities In Wireless Mesh Networks (WMNs). It Strives To Resolve The Conflicts Between The Anonymity And Traceability Objectives. In This Paper, We Attacked Sun Et Al. Scheme's Traceability. Our Analysis Showed That Trusted Authority (TA) Cannot Trace The Misbehavior Client (CL) Even If It Double-time Deposits The Same Ticket.

FIREWALL ANOMALY - Detection And Resolution Of Anomalies In Firewall Policy

A Firewall Is A System Acting As An Interface Of A Network To One Or More External Networks. It Implements The Security Policy Of The Network By Deciding Which Packets To Let Through Based On Rules Defined By The Network Administrator. Any Error In Defining The Rules May Compromise The System Security By Letting Unwanted Traffic Pass Or Blocking Desired Traffic. Manual Definition Of Rules Often Results In A Set That Contains Conflicting, Redundant Or Overshadowed Rules, Resulting In Anomalies In The Policy. Manually Detecting And Resolving These Anomalies Is A Critical But Tedious And Error Prone Task. Existing Research On This Problem Have Been Focused On The Analysis And Detection Of The Anomalies In Firewall Policy. Previous Works Define The Possible Relations Between Rules And Also Define Anomalies In Terms Of The Relations And Present Algorithms To Detect The Anomalies By Analyzing The Rules. In This Paper, We Discuss Some Necessary Modifications To The Existing Definitions Of The Relations. We Present A New Algorithm That Will Simultaneously Detect And Resolve Any Anomaly Present In The Policy Rules By Necessary Reorder And Split Operations To Generate A New Anomaly Free Rule Set. We Also Present Proof Of Correctness Of The Algorithm. Then We Present An Algorithm To Merge Rules Where Possible In Order To Reduce The Number Of Rules And Hence Increase Efficiency Of The Firewall.

WATERMARK EMBEDDING- Secure Client-Side ST-DM Watermark Embedding

Client-side Watermark Embedding Systems Have Been Proposed As A Possible Solution For The Copyright Protection In Large-scale Content Distribution Environments. In This Framework, We Propose A New Look-up-table-based Secure Client-side Embedding Scheme Properly Designed For The Spread Transform Dither Modulation Watermarking Method. A Theoretical Analysis Of The Detector Performance Under The Most Known Attack Models Is Presented And The Agreement Between Theoretical And Experimental Results Verified Through Several Simulations. The Experimental Results Also Prove That The Advantages Of The Informed Embedding Technique In Comparison To The Spread-spectrum Watermarking Approach, Which Are Well Known In The Classical Embedding Schemes, Are Preserved In The Client-side Scenario. The Proposed Approach Permits Us To Successfully Combine The Security Of Client-side Embedding With The Robustness Of Informed Embedding Methods.

FAIR CONTRACT-SIGNING PROTOCOL - An Abuse-Free Fair Contract-Signing Protocol Based On The RSA Signature

A Fair Contract-signing Protocol Allows Two Potentially Mistrusted Parities To Exchange Their Commitments (i.e., Digital Signatures) To An Agreed Contract Over The Internet In A Fair Way, So That Either Each Of Them Obtains The Other's Signature, Or Neither Party Does. Based On The RSA Signature Scheme, A New Digital Contract-signing Protocol Is Proposed In This Paper. Like The Existing RSA-based Solutions For The Same Problem, Our Protocol Is Not Only Fair, But Also Optimistic, Since The Trusted Third Party Is Involved Only In The Situations Where One Party Is Cheating Or The Communication Channel Is Interrupted. Furthermore, The Proposed Protocol Satisfies A New Property- Abuse-freeness . That Is, If The Protocol Is Executed Unsuccessfully, None Of The Two Parties Can Show The Validity Of Intermediate Results To Others. Technical Details Are Provided To Analyze The Security And Performance Of The Proposed Protocol. In Summary, We Present The First Abuse-free Fair Contract-signing Protocol Based On The RSA Signature, And Show That It Is Both Secure And Efficient.

DETECTING SPAM ZOMBIES- Detecting Spam Zombies Using Sequential Probability Ratio Test

This Paper Presents A ‘Spam Zombie Detection’ System Which Is An Online System Over The Network That Detects The Spam And The Sender Of The Spam (zombie) Before The Receiver Receives It. Thus All The Detection Work Is Done At Sender Level Itself. This Paper Focuses On A Powerful Statistical Tool Called Sequential Probability Ratio Test, Which Has Bounded False Positive And False Negative Error Rates On Which The Spam Zombie Detection System Is Based. This System Is Mainly Implemented Over The Private Mailing System. It Also Provides The Enhanced Security Mechanism In Which, If The System Which Has Been Hacked I.e. It Has Become A Zombie, Then It Gets Blocked Within The Network.

PRIVATE KEY AGAINST MEMORY DISCLOSURE ATTACKS- Mimosa Protecting Private Keys Against Memory Disclosure Attacks Using Hardware Transactional Memory

Cryptography Is Essential For Computer And Network Security. When Cryptosystems Are Deployed In Computing Or Communication Systems, It Is Extremely Critical To Protect The Cryptographic Keys. In Practice, Keys Are Loaded Into The Memory As Plaintext During Cryptographic Computations. Therefore, The Keys Are Subject To Memory Disclosure Attacks That Read Unauthorized Data From RAM. Such Attacks Could Be Performed Through Software Exploitations, Such As OpenSSL Heartbleed, Even When The Integrity Of The Victim System's Binaries Is Maintained. They Could Also Be Done Through Physical Methods, Such As Cold-boot Attacks, Even If The System Is Free Of Software Vulnerabilities. This Paper Presents Mimosa, To Protect RSA Private Keys Against Both Software-based And Physical Memory Disclosure Attacks. Mimosa Uses Hardware Transactional Memory (HTM) To Ensure That (a) Whenever A Malicious Thread Other Than Mimosa Attempts To Read The Plaintext Private Key, The Transaction Aborts And All Sensitive Data Are Automatically Cleared With Hardware, Due To The Strong Atomicity Guarantee Of HTM; And (b) All Sensitive Data, Including Private Keys And Intermediate States, Appear As Plaintext Only Within CPU-bound Caches, And Are Never Loaded To RAM Chips. To The Best Of Our Knowledge, Mimosa Is The First Solution To Use Transactional Memory To Protect Sensitive Data Against Memory Attacks. However, The Fragility Of TSX Transactions Introduces Extra Cache-clogging Denial-of-service (DoS) Threats, And Attackers Could Sharply Degrade The Performance By Concurrent Memory-intensive Tasks. To Mitigate The DoS Threats, We Further Partition An RSA Private-key Computation Into Multiple Transactional Parts By Analyzing The Distribution Of Aborts, While (sensitive) Intermediate Results Are Still Protected Across Transactional Parts. Through Extensive Experiments, We Show That Mimosa Effectively Protects Cryptographic Keys Against Attacks That Attempt To Read Sensitive Data In Memory, And Introduces Only A Small Performance Overhead, Even With Concurrent Cache-clogging Workloads.

BI-MORPHING- A Bi-Directional Bursting Defense Against Website Fingerprinting Attacks

Network Traffic Analysis Has Been Increasingly Used In Various Applications To Either Protect Or Threaten People, Information, And Systems. Website Fingerprinting Is A Passive Traffic Analysis Attack Which Threatens Web Navigation Privacy. It Is A Set Of Techniques Used To Discover Patterns From A Sequence Of Network Packets Generated While A User Accesses Different Websites. Internet Users (such As Online Activists Or Journalists) May Wish To Hide Their Identity And Online Activity To Protect Their Privacy. Typically, An Anonymity Network Is Utilized For This Purpose. These Anonymity Networks Such As Tor (The Onion Router) Provide Layers Of Data Encryption Which Poses A Challenge To The Traffic Analysis Techniques. Although Various Defenses Have Been Proposed To Counteract This Passive Attack, They Have Been Penetrated By New Attacks That Proved The Ineffectiveness And/or Impracticality Of Such Defenses. In This Work, We Introduce A Novel Defense Algorithm To Counteract The Website Fingerprinting Attacks. The Proposed Defense Obfuscates Original Website Traffic Patterns Through The Use Of Double Sampling And Mathematical Optimization Techniques To Deform Packet Sequences And Destroy Traffic Flow Dependency Characteristics Used By Attackers To Identify Websites. We Evaluate Our Defense Against State-of-the-art Studies And Show Its Effectiveness With Minimal Overhead And Zero-delay Transmission To The Real Traffic.

FAULT DETECTION FILTER- Fault Detection Filter And Controller Co-Design For Unmanned Surface Vehicles Under DoS Attacks

This Paper Addresses The Co-design Problem Of A Fault Detection Filter And Controller For A Networked-based Unmanned Surface Vehicle (USV) System Subject To Communication Delays, External Disturbance, Faults, And Aperiodic Denial-of-service (DoS) Jamming Attacks. First, An Event-triggering Communication Scheme Is Proposed To Enhance The Efficiency Of Network Resource Utilization While Counteracting The Impact Of Aperiodic DoS Attacks On The USV Control System Performance. Second, An Event-based Switched USV Control System Is Presented To Account For The Simultaneous Presence Of Communication Delays, Disturbance, Faults, And DoS Jamming Attacks. Third, By Using The Piecewise Lyapunov Functional (PLF) Approach, Criteria For Exponential Stability Analysis And Co-design Of A Desired Observer-based Fault Detection Filter And An Event-triggered Controller Are Derived And Expressed In Terms Of Linear Matrix Inequalities (LMIs). Finally, The Simulation Results Verify The Effectiveness Of The Proposed Co-design Method. The Results Show That This Method Not Only Ensures The Safe And Stable Operation Of The USV But Also Reduces The Amount Of Data Transmissions.

Modeling And Detection Of Flooding-Based Denial-of-Service Attack In Wireless Ad Hoc Network Using Bayesian Inference

Wireless Ad Hoc Networks Are Widely Useful In Locations Where The Existing Infrastructure Is Difficult To Use, Especially During The Situations Like Flood, Earthquakes, And Other Natural Or Man-made Calamities. Lack Of Centralized Management And Absence Of Secure Boundaries Make These Networks Vulnerable To Various Types Of Attacks. Moreover, The Mobile Nodes Used In These Networks Have Limited Computational Capability, Memory, And Battery Backup. Flooding-based Denial-of-service (DoS) Attack, Which Results In Denial Of Sleep Attack, Targets The Mobile Node's Constrained Resources Which Results In Excess Consumption Of Battery Backup. In SYN Flooding-based DoS Attack, The Attacker Sends A Large Number Of Spoofed SYN Packets Which Not Only Overflow The Target Buffer But Also Creates Network Congestion. The Present Article Is Divided Into Three Parts: 1) Mathematical Modeling For SYN Traffic In The Network Using Bayesian Inference; 2) Proving The Equivalence Of Bayesian Inference With Exponential Weighted Moving Average; And 3) Developing An Efficient Algorithm For The Detection Of SYN Flooding Attack Using Bayesian Inference. Based On The Comprehensive Evaluation Using Mathematical Modeling And Simulation, The Proposed Method Can Successfully Defend Any Type Of Flooding-based DoS Attack In Wireless Ad Hoc Network With Higher Detection Accuracy And Extremely Lower False Detection Rate.

SQL INJECTION ATTACK - A Detection And Prevention Technique On SQL Injection Attacks

With The Web Advancements Are Rapidly Developing, The Greater Part Of Individuals Makes Their Transactions On Web, For Example, Searching Through Data, Banking, Shopping, Managing, Overseeing And Controlling Dam And Business Exchanges, Etc. Web Applications Have Gotten Fit To Numerous Individuals' Day By Day Lives Activities. Dangers Pertinent To Web Applications Have Expanded To Huge Development. Presently A Day, The More The Quantity Of Vulnerabilities Will Be Diminished, The More The Quantity Of Threats Become To Increment. Structured Query Language Injection Attack (SQLIA) Is One Of The Incredible Dangers Of Web Applications Threats. Lack Of Input Validation Vulnerabilities Where Cause To SQL Injection Attack On Web. SQLIA Is A Malicious Activity That Takes Negated SQL Statement To Misuse Data-driven Applications. This Vulnerability Admits An Attacker To Comply Crafted Input To Disclosure With The Application's Interaction With Back-end Databases. Therefore, The Attacker Can Gain Access To The Database By Inserting, Modifying Or Deleting Critical Information Without Legitimate Approval. The Paper Presents An Approach Which Detects A Query Token With Reserved Words-based Lexicon To Detect SQLIA. The Approach Consists Of Two Highlights: The First One Creates Lexicon And The Second Step Tokenizes The Input Query Statement And Each String Token Was Detected To Predefined Words Lexicon To Prevent SQLIA. In This Paper, Detection And Prevention Technologies Of SQL Injection Attacks Are Experimented And The Result Are Satisfactory.

INTERRUPTION DETECTION- Resourceful Interruption Detection With Blossom Filtering In Manager Area Networks

Due To Its Cost Efficiency The Controller Area Network (CAN) Is Still The Most Wide-spread In-vehicle Bus And The Numerous Reported Attacks Demonstrate The Urgency In Designing New Security Solutions For CAN. In This Work We Propose An Intrusion Detection Mechanism That Takes Advantage Of Bloom Filtering To Test Frame Periodicity Based On Message Identifiers And Parts Of The Data-field Which Facilitates Detection Of Potential Replay Or Modification Attacks. This Proves To Be An Effective Approach Since Most Of The Traffic From In-vehicle Buses Is Cyclic In Nature And The Format Of The Data-field Is Fixed Due To Rigid Signal Allocation. Bloom Filters Provide An Efficient Time-memory Tradeoff Which Is Beneficial For The Constrained Resources Of Automotive Grade Controllers. We Test The Correctness Of Our Approach And Obtain Good Results On An Industry-standard CANoe Based Simulation For A J1939 Commercial-vehicle Bus And Also On CAN-FD Traces Obtained From A Real-world High-end Vehicle. The Proposed Filtering Mechanism Is Straight-forward To Adapt For Any Other Time-triggered In-vehicle Bus, E.g., FlexRay, Since It Is Built On Time-driven Characteristics.

STEGANOGRAPHY TECHNIQUES- Hiding Data Using Efficient Combination Of RSA Cryptography And Compression Steganography Techniques

Data Compression Is An Important Part Of Information Security Because Compressed Data Is More Secure And Easy To Handle. Effective Data Compression Technology Creates Efficient, Secure, And Easy-to-connect Data. There Are Two Types Of Compression Algorithm Techniques, Lossy And Lossless. These Technologies Can Be Used In Any Data Format Such As Text, Audio, Video, Or Image File. The Main Objective Of This Study Was To Reduce The Physical Space On The Various Storage Media And Reduce The Time Of Sending Data Over The Internet With A Complete Guarantee Of Encrypting This Data And Hiding It From Intruders. Two Techniques Are Implemented, With Data Loss (Lossy) And Without Data Loss (Lossless). In The Proposed Paper A Hybrid Data Compression Algorithm Increases The Input Data To Be Encrypted By RSA (Rivest-Shamir-Adleman) Cryptography Method To Enhance The Security Level And It Can Be Used In Executing Lossy And Lossless Compacting Steganography Methods. This Technique Can Be Used To Decrease The Amount Of Every Transmitted Data Aiding Fast Transmission While Using Slow Internet Or Take A Small Space On Different Storage Media. The Plain Text Is Compressed By The Huffman Coding Algorithm, And Also The Cover Image Is Compressed By Discrete Wavelet Transform DWT Based That Compacts The Cover Image Through Lossy Compression In Order To Reduce The Cover Image's Dimensions. The Least Significant Bit LSB Will Then Be Used To Implant The Encrypted Data In The Compacted Cover Image. We Evaluated That System On Criteria Such As Percentage Savings Percentage, Compression Time, Compression Ratio, Bits Per Pixel, Mean Squared Error, Peak Signal To Noise Ratio, Structural Similarity Index, And Compression Speed. This System Shows A High-level Performance And System Methodology Compared To Other Systems That Use The Same Methodology.

PROFILE-BASED CLUSTER EVOLUTION ANALYSIS- Identification Of Migration Patterns For Understanding Student Learning Behavior

Educational Process Mining Is One Of The Research Domains That Utilizes Students' Learning Behavior To Match Students' Actual Courses Taken And The Designed Curriculum. While Most Works Attempt To Deal With The Case Perspective (i.e., Traces Of The Cases), The Temporal Case Perspective Has Not Been Discussed. The Temporal Case Perspective Aims To Understand The Temporal Patterns Of Cases (e.g., Students' Learning Behavior In A Semester). This Study Proposes Modified Cluster Evolution Analysis, Called Profile-based Cluster Evolution Analysis, For Students' Learning Behavior Based On Profiles. The Results Show Three Salient Features: (1) Cluster Generation; (2) Within-cluster Generation; And (3) Time-based Between-cluster Generation. The Cluster Evolution Phase Modifies The Existing Cluster Evolution Analysis With A Dynamic Profiler. The Model Was Tested On Actual Educational Data Of The Information System Department In Indonesia. The Results Showed The Learning Behavior Of Students Who Graduated On Time, The Learning Behavior Of Students Who Graduated Late, And The Learning Behavior Of Students Who Dropped Out. Students Changed Their Learning Behavior By Observing The Migration Of Students From Cluster To Cluster For Each Semester. Furthermore, There Were Distinct Learning Behavior Migration Patterns For Each Category Of Students Based On Their Performance. The Migration Pattern Can Suggest To Academic Stakeholders To Understand About Students Who Are Likely To Drop Out, Graduate On Time Or Graduate Late. These Results Can Be Used As Recommendations To Academic Stakeholders For Curriculum Assessment And Development And Dropout Prevention.

NETSPAM- Spam Review Detection Using The Linguistic And Spammer Behavioral Methods

Online Reviews Regarding Different Products Or Services Have Become The Main Source To Determine Public Opinions. Consequently, Manufacturers And Sellers Are Extremely Concerned With Customer Reviews As These Have A Direct Impact On Their Businesses. Unfortunately, To Gain Profits Or Fame, Spam Reviews Are Written To Promote Or Demote Targeted Products Or Services. This Practice Is Known As Review Spamming. In Recent Years, The Spam Review Detection Problem Has Gained Much Attention From Communities And Researchers, But Still There Is A Need To Perform Experiments On Real-world Large-scale Review Datasets. This Can Help To Analyze The Impact Of Widespread Opinion Spam In Online Reviews. In This Work, Two Different Spam Review Detection Methods Have Been Proposed: (1) Spam Review Detection Using Behavioral Method (SRD-BM) Utilizes Thirteen Different Spammer's Behavioral Features To Calculate The Review Spam Score Which Is Then Used To Identify Spammers And Spam Reviews, And (2) Spam Review Detection Using Linguistic Method (SRD-LM) Works On The Content Of The Reviews And Utilizes Transformation, Feature Selection And Classification To Identify The Spam Reviews. Experimental Evaluations Are Conducted On A Real-world Amazon Review Dataset Which Analyze 26.7 Million Reviews And 15.4 Million Reviewers. The Evaluations Show That Both Proposed Models Have Significantly Improved The Detection Process Of Spam Reviews. Specifically, SRD-BM Achieved 93.1% Accuracy Whereas SRD-LM Achieved 88.5% Accuracy In Spam Review Detection. Comparatively, SRD-BM Achieved Better Accuracy Because It Works On Utilizing Rich Set Of Spammers Behavioral Features Of Review Dataset Which Provides In-depth Analysis Of Spammer Behaviour. Moreover, Both Proposed Models Outperformed Existing Approaches When Compared In Terms Of Accurate Identification Of Spam Reviews. To The Best Of Our Knowledge, This Is The First Study Of Its Kind Which Uses Large-scale Review Dataset To Analyze Different Spammers' Behavioral Features And Linguistic Method Utilizing Different Available Classifiers.

DEEP REPRESENTATION BASED FEATURE EXTRACTION AND RECOVERING- Efficient Finger Vein Technology Based On Fast Binary Robust Independent Elementary Feature Combined With Multi-Image Quality Assessment Verification

Finger-vein Biometrics Has Been Extensively Investigated For Personal Verification. Despite Recent Advances In Fingervein Verification, Current Solutions Completely Depend On Domain Knowledge And Still Lack The Robustness To Extract Finger-vein Features From Raw Images. This Paper Proposes A Deep Learning Model To Extract And Recover Vein Features Using Limited A Priori Knowledge. Firstly, Based On A Combination Of Known State Of The Art Handcrafted Finger-vein Image Segmentation Techniques, We Automatically Identify Two Regions: A Clear Region With High Separability Between Finger-vein Patterns And Background, And An Ambiguous Region With Low Separability Between Them. The First Is Associated With Pixels On Which All The Segmentation Techniques Above Assign The Same Segmentation Label (either Foreground Or Background), While The Second Corresponds To All The Remaining Pixels. This Scheme Is Used To Automatically Discard The Ambiguous Region And To Label The Pixels Of The Clear Region As Foreground Or Background. A Training Dataset Is Constructed Based On The Patches Centered On The Labeled Pixels. Secondly, A Convolutional Neural Network (CNN) Is Trained On The Resulting Dataset To Predict The Probability Of Each Pixel Of Being Foreground (i.e. Vein Pixel) Given A Patch Centered On It. The CNN Learns What A Fingervein Pattern Is By Learning The Difference Between Vein Patterns And Background Ones. The Pixels In Any Region Of A Test Image Can Then Be Classified Effectively. Thirdly, We Propose Another New And Original Contribution By Developing And Investigating A Fully Convolutional Network (FCN) To Recover Missing Fingervein Patterns In The Segmented Image. The Experimental Results On Two Public Finger-vein Databases Show A Significant Improvement In Terms Of Finger-vein Verification Accuracy.

OUTSOURCED DATA STREAMS UNDER MULTIPLE KEYS- KV-Fresh Freshness Authentication For Outsourced Multi-Version Key-Value Stores

Data Outsourcing Is A Promising Technical Paradigm To Facilitate Cost-effective Real-time Data Storage, Processing, And Dissemination. In Such A System, A Data Owner Proactively Pushes A Stream Of Data Records To A Third-party Cloud Server For Storage, Which In Turn Processes Various Types Of Queries From End Users On The Data Owner’s Behalf. This Paper Considers Outsourced Multi-version Key-value Stores That Have Gained Increasing Popularity In Recent Years, Where A Critical Security Challenge Is To Ensure That The Cloud Server Returns Both Authentic And Fresh Data In Response To End Users’ Queries. Despite Several Recent Attempts On Authenticating Data Freshness In Outsourced Key-value Stores, They Either Incur Excessively High Communication Cost Or Can Only Offer Very Limited Real-time Guarantee. To Fill This Gap, This Paper Introduces KV-Fresh, A Novel Freshness Authentication Scheme For Outsourced Key-value Stores That Offers Strong Real-time Guarantee. KV-Fresh Is Designed Based On A Novel Data Structure, Linked Key Span Merkle Hash Tree, Which Enables Highly Efficient Freshness Proof By Embedding Chaining Relationship Among Records Generated At Different Time. Detailed Simulation Studies Using A Synthetic Dataset Generated From Real Data Confirm The Efficacy And Efficiency Of KV-Fresh.

MAJORITY VOTING AND PARING- Majority Voting And Paring With Multiple Noisy Labeling

With The Crowdsourcing Of Small Tasks Becoming Easier, It Is Possible To Obtain Non-expert/imperfect Labels At Low Cost. With Low-cost Imperfect Labeling, It Is Straightforward To Collect Multiple Labels For The Same Data Items. This Paper Proposes Strategies Of Utilizing These Multiple Labels For Supervised Learning, Based On Two Basic Ideas: Majority Voting And Pairing. We Show Several Interesting Results Based On Our Experiments. (i) The Strategies Based On The Majority Voting Idea Work Well Under The Situation Where The Certainty Level Is High. (ii) On The Contrary, The Pairing Strategies Are More Preferable Under The Situation Where The Certainty Level Is Low. (iii) Among The Majority Voting Strategies, Soft Majority Voting Can Reduce The Bias And Roughness, And Perform Better Than Majority Voting. (iv) Pairing Can Completely Avoid The Bias By Having Both Sides (potentially Correct And Incorrect/noisy Information) Considered. Beta Estimation Is Applied To Reduce The Impact Of The Noise In Pairing. Our Experimental Results Show That Pairing With Beta Estimation Always Performs Well Under Different Certainty Levels. (v) All Strategies Investigated Are Labeling Quality Agnostic Strategies For Real-world Applications, And Some Of Them Perform Better Than Or At Least Very Close To The Gnostic Strategies.

ONLINE VOTING SYSTEM WITH RELIABLE VOTER- Online Voting System With Reliable Voter Authentication Protocol

A Detailed And Critical Analysis Was Done On Manual And E-voting Systems Implemented. These Systems Exhibited Weaknesses Of Unreliable Protocols, Denial Of Service Attacks Hence The Need To Implement The Public-key Encryption E-voting System. Using Makerere University As A Case Study, The Major Aim Of The Public-key Encryption E-voting System Is To Assure Reliability And Security Of The Protocol Hence Guaranteeing Voting Convenience. Interviews And Document Review Were Used To Determine Inputs, Processes And Outputs. As A Result Of The Requirements Specification, The System Was Summarized Into Three Processes: Access Control Process Which Involves Identification And Authentication Phases For Eligible Voters. Secondly, The Voting Process Was Done By Encrypting Voter's Electronic Ballot Before Submitting To The Server. Finally, The Final Result Was Sorted Through Deciphering The Received Encrypted Information. The System Is More Efficient Than Other E-Voting Systems Since Voters Can Vote From Their Devices Without Extra Cost And Effort, And Encryption Ensures The Security.

COVID-19 PANDEMIC- A Systematic Review On The Use Of AI And ML For Fighting The COVID-19 Pandemic

Artificial Intelligence (AI) And Machine Learning (ML) Have Caused A Paradigm Shift In Healthcare That Can Be Used For Decision Support And Forecasting By Exploring Medical Data. Recent Studies Have Shown That AI And ML Can Be Used To Fight COVID-19. The Objective Of This Article Is To Summarize The Recent AI- And ML-based Studies That Have Addressed The Pandemic. From An Initial Set Of 634 Articles, A Total Of 49 Articles Were Finally Selected Through An Inclusion-exclusion Process. In This Article, We Have Explored The Objectives Of The Existing Studies (i.e., The Role Of AI/ML In Fighting The COVID-19 Pandemic); The Context Of The Studies (i.e., Whether It Was Focused On A Specific Country-context Or With A Global Perspective; The Type And Volume Of The Dataset; And The Methodology, Algorithms, And Techniques Adopted In The Prediction Or Diagnosis Processes). We Have Mapped The Algorithms And Techniques With The Data Type By Highlighting Their Prediction/classification Accuracy. From Our Analysis, We Categorized The Objectives Of The Studies Into Four Groups: Disease Detection, Epidemic Forecasting, Sustainable Development, And Disease Diagnosis. We Observed That Most Of These Studies Used Deep Learning Algorithms On Image-data, More Specifically On Chest X-rays And CT Scans. We Have Identified Six Future Research Opportunities That We Have Summarized In This Paper.

FACE EXPRESSION RECOGNITION- Automatic Detection Of Pain From Facial Expressions- A Survey

Pain Sensation Is Essential For Survival, Since It Draws Attention To Physical Threat To The Body. Pain Assessment Is Usually Done Through Self-reports. However, Self-assessment Of Pain Is Not Available In The Case Of Noncommunicative Patients, And Therefore, Observer Reports Should Be Relied Upon. Observer Reports Of Pain Could Be Prone To Errors Due To Subjective Biases Of Observers. Moreover, Continuous Monitoring By Humans Is Impractical. Therefore, Automatic Pain Detection Technology Could Be Deployed To Assist Human Caregivers And Complement Their Service, Thereby Improving The Quality Of Pain Management, Especially For Noncommunicative Patients. Facial Expressions Are A Reliable Indicator Of Pain, And Are Used In All Observer-based Pain Assessment Tools. Following The Advancements In Automatic Facial Expression Analysis, Computer Vision Researchers Have Tried To Use This Technology For Developing Approaches For Automatically Detecting Pain From Facial Expressions. This Paper Surveys The Literature Published In This Field Over The Past Decade, Categorizes It, And Identifies Future Research Directions. The Survey Covers The Pain Datasets Used In The Reviewed Literature, The Learning Tasks Targeted By The Approaches, The Features Extracted From Images And Image Sequences To Represent Pain-related Information, And Finally, The Machine Learning Methods Used.

EHEALTHCARE RECOMMENDATION SCHEME- A Privacy-Preserving Online Medical Service Recommendation Scheme In E-Healthcare System

With The Continuous Development Of EHealthcare Systems, Medical Service Recommendation Has Received Great Attention. However, Although It Can Recommend Doctors To Users, There Are Still Challenges In Ensuring The Accuracy And Privacy Of Recommendation. In This Paper, To Ensure The Accuracy Of The Recommendation, We Consider Doctors’ Reputation Scores And Similarities Between Users’ Demands And Doctors’ Information As The Basis Of The Medical Service Recommendation. The Doctors’ Reputation Scores Are Measured By Multiple Feedbacks From Users. We Propose Two Concrete Algorithms To Compute The Similarity And The Reputation Scores In A Privacy-preserving Way Based On The Modified Paillier Cryptosystem, Truth Discovery Technology, And The Dirichlet Distribution. Detailed Security Analysis Is Given To Show Its Security Prosperities. In Addition, Extensive Experiments Demonstrate The Efficiency In Terms Of Computational Time For Truth Discovery And Recommendation Process.

REMOTE AUTHENTICATION SCHEMES- Remote Authentication Schemes For Wireless Body Area Networks Based On The Internet Of Things

Internet Of Things (IoT) Is A New Technology Which Offers Enormous Applications That Make People’s Lives More Convenient And Enhances Cities’ Development. In Particular, Smart Healthcare Applications In IoT Have Been Receiving Increasing Attention For Industrial And Academic Research. However, Due To The Sensitiveness Of Medical Information, Security And Privacy Issues In IoT Healthcare Systems Are Very Important. Designing An Efficient Secure Scheme With Less Computation Time And Energy Consumption Is A Critical Challenge In IoT Healthcare Systems. In This Paper, A Lightweight Online/offline Certificateless Signature (L-OOCLS) Is Proposed, Then A Heterogeneous Remote Anonymous Authentication Protocol (HRAAP) Is Designed To Enable Remote Wireless Body Area Networks (WBANs) Users To Anonymously Enjoy Healthcare Service Based On The IoT Applications. The Proposed L-OOCLS Scheme Is Proven Secure In Random Oracle Model And The Proposed HRAAP Can Resist Various Types Of Attacks. Compared With The Existing Relevant Schemes, The Proposed HRAAP Achieves Less Computation Overhead As Well As Less Power Consumption On WBANs Client. In Addition, To Nicely Meet The Application In The IoT, An Application Scenario Is Given.

Dynamic Rumor Influence Minimization With User Experience In Social Networks

Dynamic Rumor Influence Minimization With User Experience In Social NetworksWith The Soaring Development Of Large Scale Online Social Networks, Online Information Sharing Is Becoming Ubiquitous Everyday. Various Information Is Propagating Through Online Social Networks Including Both The Positive And Negative. In This Paper, We Focus On The Negative Information Problems Such As The Online Rumors. Rumor Blocking Is A Serious Problem In Large-scale Social Networks. Malicious Rumors Could Cause Chaos In Society And Hence Need To Be Blocked As Soon As Possible After Being Detected. In This Paper, We Propose A Model Of Dynamic Rumor Influence Minimization With User Experience (DRIMUX). Our Goal Is To Minimize The Influence Of The Rumor (i.e., The Number Of Users That Have Accepted And Sent The Rumor) By Blocking A Certain Subset Of Nodes. A Dynamic Ising Propagation Model Considering Both The Global Popularity And Individual Attraction Of The Rumor Is Presented Based On A Realistic Scenario. In Addition, Different From Existing Problems Of Influence Minimization, We Take Into Account The Constraint Of User Experience Utility. Specifically, Each Node Is Assigned A Tolerance Time Threshold. If The Blocking Time Of Each User Exceeds That Threshold, The Utility Of The Network Will Decrease. Under This Constraint, We Then Formulate The Problem As A Network Inference Problem With Survival Theory, And Propose Solutions Based On Maximum Likelihood Principle. Experiments Are Implemented Based On Large-scale Real World Networks And Validate The Effectiveness Of Our Method.

ATTRIBUTE BASED SIGNATURE- Outsourced Decentralized Multi-Authority Attribute Based Signature And Its Application In IOT

IoT (Internet Of Things) Devices Often Collect Data And Store The Data In The Cloud For Sharing And Further Processing; This Collection, Sharing, And Processing Will Inevitably Encounter Secure Access And Authentication Issues. Attribute Based Signature (ABS), Which Utilizes The Signer’s Attributes To Generate Private Keys, Plays A Competent Role In Data Authentication And Identity Privacy Preservation. In ABS, There Are Multiple Authorities That Issue Different Private Keys For Signers Based On Their Various Attributes, And A Central Authority Is Usually Established To Manage All These Attribute Authorities. However, One Security Concern Is That If The Central Authority Is Compromised, The Whole System Will Be Broken. In This Paper, We Present An Outsourced Decentralized Multi-authority Attribute Based Signature (ODMA-ABS) Scheme. The Proposed ODMA-ABS Achieves Attribute Privacy And Stronger Authority-corruption Resistance Than Existing Multi-authority Attribute Based Signature Schemes Can Achieve. In Addition, The Overhead To Generate A Signature Is Further Reduced By Outsourcing Expensive Computation To A Signing Cloud Server. We Present Extensive Security Analysis And Experimental Simulation Of The Proposed Scheme. We Also Propose An Access Control Scheme That Is Based On ODMA-ABS.

TO ENGAGE STUDENT IN SOCIAL MEDIA NETWORK- Creating And Using Digital Games For Learning In Elementary And Secondary Education

The Use Of Digital Games In Education Has Gained Considerable Popularity In The Last Years Due To The Fact That These Games Are Considered To Be Excellent Tools For Teaching And Learning And Offer To Students An Engaging And Interesting Way Of Participating And Learning. In This Study, The Design And Implementation Of Educational Activities That Include Game Creation And Use In Elementary And Secondary Education Is Presented. The Proposed Educational Activities’ Content Covers The Parts Of The Curricula Of All The Informatics Courses, For Each Education Level Separately, That Include The Learning Of Programming Principles. The Educational Activities Were Implemented And Evaluated By Teachers Through A Discussion Session. The Findings Indicate That The Teachers Think That Learning Through Creating And Using Games Is More Interesting And That They Also Like The Idea Of Using Various Programming Environments To Create Games In Order To Teach Basic Programming Principles To Students.

USER PROFILE MATCHING- Privacy-Preserving User Profile Matching In Social Networks

In This Paper, We Consider A Scenario Where A User Queries A User Profile Database, Maintained By A Social Networking Service Provider, To Identify Users Whose Profiles Match The Profile Specified By The Querying User. A Typical Example Of This Application Is Online Dating. Most Recently, An Online Dating Website, Ashley Madison, Was Hacked, Which Resulted In A Disclosure Of A Large Number Of Dating User Profiles. This Data Breach Has Urged Researchers To Explore Practical Privacy Protection For User Profiles In A Social Network. In This Paper, We Propose A Privacy-preserving Solution For Profile Matching In Social Networks By Using Multiple Servers. Our Solution Is Built On Homomorphic Encryption And Allows A User To Find Out Matching Users With The Help Of Multiple Servers Without Revealing To Anyone The Query And The Queried User Profiles In Clear. Our Solution Achieves User Profile Privacy And User Query Privacy As Long As At Least One Of The Multiple Servers Is Honest. Our Experiments Demonstrate That Our Solution Is Practical.

ONLINE SOCAIL NETWORKS- Two Tales Of Privacy In Online Social Networks

Privacy Is One Of The Friction Points That Emerges When Communications Get Mediated In Online Social Networks (OSNs). Different Communities Of Computer Science Researchers Have Framed The ‘OSN Privacy Problem’ As One Of Surveillance, Institutional Or Social Privacy. In Tackling These Problems They Have Also Treated Them As If They Were Independent. We Argue That The Different Privacy Problems Are Entangled And That Research On Privacy In OSNs Would Benefit From A More Holistic Approach. In This Article, We First Provide An Introduction To The Surveillance And Social Privacy Perspectives Emphasizing The Narratives That Inform Them, As Well As Their Assumptions, Goals And Methods. We Then Juxtapose The Differences Between These Two Approaches In Order To Understand Their Complementarity, And To Identify Potential Integration Challenges As Well As Research Questions That So Far Have Been Left Unanswered.

NETSPAM- Spam Review Detection Using The Linguistic And Spammer Behavioral Methods

Online Reviews Regarding Different Products Or Services Have Become The Main Source To Determine Public Opinions. Unfortunately, To Gain Profits Or Fame, Spam Reviews Are Written To Promote Or Demote Targeted Products Or Services. This Practice Is Known As Review Spamming. This Can Help To Analyze The Impact Of Widespread Opinion Spam In Online Reviews. In This Work, Two Different Spam Review Detection Methods Have Been Proposed: (1) Spam Review Detection Using Behavioral Method (SRD-BM) Utilizes Thirteen Different Spammer’s Behavioral Features To Calculate The Review Spam Score Which Is Then Used To Identify Spammers And Spam Reviews, And (2) Spam Review Detection Using Linguistic Method (SRD-LM) Works On The Content Of The Reviews And Utilizes Transformation, Feature Selection And Classification To Identify The Spam Reviews. Experimental Evaluations Are Conducted On A Real-world Amazon Review Dataset Which Analyze 26.7 Million Reviews And 15.4 Million Reviewers. The Evaluations Show That Both Proposed Models Have Significantly Improved The Detection Process Of Spam Reviews. Comparatively, SRD-BM Achieved Better Accuracy Because It Works On Utilizing Rich Set Of Spammers Behavioral Features Of Review Dataset Which Provides In-depth Analysis Of Spammer Behavior. To The Best Of Our Knowledge, This Is The First Study Of Its Kind Which Uses Large-scale Review Dataset To Analyze Different Spammers’ Behavioral Features And Linguistic Method Utilizing Different Available Classifiers.

DEEP REPRESENTATION BASED FEATURE EXTRACTION AND RECOVERING- Efficient Finger Vein Technology Based On Fast Binary Robust Independent Elementary Feature Combined With Multi-Image Quality Assessment Verification

Finger-vein Biometrics Has Been Extensively Investigated For Personal Verification. Despite Recent Advances In Fingervein Verification, Current Solutions Completely Depend On Domain Knowledge And Still Lack The Robustness To Extract Finger-vein Features From Raw Images. This Paper Proposes A Deep Learning Model To Extract And Recover Vein Features Using Limited A Priori Knowledge. Firstly, Based On A Combination Of Known State Of The Art Handcrafted Finger-vein Image Segmentation Techniques, We Automatically Identify Two Regions: A Clear Region With High Separability Between Finger-vein Patterns And Background, And An Ambiguous Region With Low Separability Between Them. The First Is Associated With Pixels On Which All The Segmentation Techniques Above Assign The Same Segmentation Label (either Foreground Or Background), While The Second Corresponds To All The Remaining Pixels. This Scheme Is Used To Automatically Discard The Ambiguous Region And To Label The Pixels Of The Clear Region As Foreground Or Background. A Training Dataset Is Constructed Based On The Patches Centered On The Labeled Pixels. Secondly, A Convolutional Neural Network (CNN) Is Trained On The Resulting Dataset To Predict The Probability Of Each Pixel Of Being Foreground (i.e. Vein Pixel) Given A Patch Centered On It. The CNN Learns What A Fingervein Pattern Is By Learning The Difference Between Vein Patterns And Background Ones. The Pixels In Any Region Of A Test Image Can Then Be Classified Effectively. Thirdly, We Propose Another New And Original Contribution By Developing And Investigating A Fully Convolutional Network (FCN) To Recover Missing Fingervein Patterns In The Segmented Image. The Experimental Results On Two Public Finger-vein Databases Show A Significant Improvement In Terms Of Finger-vein Verification Accuracy.

OUTSOURCED DATA STREAMS UNDER MULTIPLE KEYS- KV-Fresh Freshness Authentication For Outsourced Multi-Version Key-Value Stores

Data Outsourcing Is A Promising Technical Paradigm To Facilitate Cost-effective Real-time Data Storage, Processing, And Dissemination. In Such A System, A Data Owner Proactively Pushes A Stream Of Data Records To A Third-party Cloud Server For Storage, Which In Turn Processes Various Types Of Queries From End Users On The Data Owner’s Behalf. This Paper Considers Outsourced Multi-version Key-value Stores That Have Gained Increasing Popularity In Recent Years, Where A Critical Security Challenge Is To Ensure That The Cloud Server Returns Both Authentic And Fresh Data In Response To End Users’ Queries. Despite Several Recent Attempts On Authenticating Data Freshness In Outsourced Key-value Stores, They Either Incur Excessively High Communication Cost Or Can Only Offer Very Limited Real-time Guarantee. To Fill This Gap, This Paper Introduces KV-Fresh, A Novel Freshness Authentication Scheme For Outsourced Key-value Stores That Offers Strong Real-time Guarantee. KV-Fresh Is Designed Based On A Novel Data Structure, Linked Key Span Merkle Hash Tree, Which Enables Highly Efficient Freshness Proof By Embedding Chaining Relationship Among Records Generated At Different Time. Detailed Simulation Studies Using A Synthetic Dataset Generated From Real Data Confirm The Efficacy And Efficiency Of KV-Fresh.

ATTRIBUTE BASED STORAGE SUPPORTING SECURE DEDUPLICATION- KeyD Secure Key-Deduplication With Identity-Based Broadcast Encryption

Deduplication Enables Us To Store Only One Copy Of Identical Data And Becomes Unprecedentedly Significant With The Dramatic Increase In Data Stored In The Cloud. For The Purpose Of Ensuring Data Confidentiality, They Are Usually Encrypted Before Outsourced. Traditional Encryption Will Inevitably Result In Multiple Different Ciphertexts Produced From The Same Plaintext By Different Users' Secret Keys, Which Hinders Data Deduplication. Convergent Encryption Makes Deduplication Possible Since It Naturally Encrypts The Same Plaintexts Into The Same Ciphertexts. One Attendant Problem Is How To Effectively Manage A Huge Number Of Convergent Keys. Several Deduplication Schemes Have Been Proposed To Deal With This Problem. However, They Either Need To Introduce Key Management Servers Or Require Interaction Between Data Owners. In This Paper, We Design A Novel Client-side Deduplication Protocol Named KeyD Without Such An Independent Key Management Server By Utilizing The Identity-based Broadcast Encryption (IBBE) Technique. Users Only Interact With The Cloud Service Provider (CSP) During The Process Of Data Upload And Download. Security Analysis Demonstrates That KeyD Ensures Data Confidentiality And Convergent Key Security, And Well Protects The Ownership Privacy Simultaneously. A Thorough Performance Comparison Shows That Our Scheme Makes A Better Tradeoff Among The Storage Cost, Communication And Computation Overhead.

SECURING AGGREGATE - Securing Aggregate Queries For DNA Databases

This Paper Addresses The Problem Of Sharing Person-specific Genomic Sequences Without Violating The Privacy Of Their Data Subjects To Support Large-scale Biomedical Research Projects. The Proposed Method Builds On The Framework Proposed By Kantarcioglu Et Al. [1] But Extends The Results In A Number Of Ways. One Improvement Is That Our Scheme Is Deterministic, With Zero Probability Of A Wrong Answer (as Opposed To A Low Probability). We Also Provide A New Operating Point In The Space-time Tradeoff, By Offering A Scheme That Is Twice As Fast As Theirs But Uses Twice The Storage Space. This Point Is Motivated By The Fact That Storage Is Cheaper Than Computation In Current Cloud Computing Pricing Plans. Moreover, Our Encoding Of The Data Makes It Possible For Us To Handle A Richer Set Of Queries Than Exact Matching Between The Query And Each Sequence Of The Database, Including: (i) Counting The Number Of Matches Between The Query Symbols And A Sequence; (ii) Logical OR Matches Where A Query Symbol Is Allowed To Match A Subset Of The Alphabet Thereby Making It Possible To Handle (as A Special Case) A “not Equal To” Requirement For A Query Symbol (e.g., “not A G”); (iii) Support For The Extended Alphabet Of Nucleotide Base Codes That Encompasses Ambiguities In DNA Sequences (this Happens On The DNA Sequence Side Instead Of The Query Side); (iv) Queries That Specify The Number Of Occurrences Of Each Kind Of Symbol In The Specified Sequence Positions (e.g., Two `A' And Four `C' And One `G' And Three `T', Occurring In Any Order In The Query-specified Sequence Positions); (v) A Threshold Query Whose Answer Is `yes' If The Number Of Matches Exceeds A Query-specified Threshold (e.g., “7 Or More Matches Out Of The 15 Query-specified Positions”). (vi) For All Query Types, We Can Hide The Answers From The Decrypting Server, So That Only The Client Learns The Answer. (vii) In All Cases, The Client Deterministically Learns Only The Query's Answer, Except For Query Type (v) Where We Quantify The (very Small) Statistical Leakage To The Client Of The Actual Count.

SECURE AND DYNAMIC MULTI-KEYWORD RANKED SEARCH SCHEME - A Key-Policy Attribute-Based Temporary Keyword Search Scheme For Secure Cloud Storage

Temporary Keyword Search On Confidential Data In A Cloud Environment Is The Main Focus Of This Research. The Cloud Providers Are Not Fully Trusted. So, It Is Necessary To Outsource Data In The Encrypted Form. In The Attribute-based Keyword Search (ABKS) Schemes, The Authorized Users Can Generate Some Search Tokens And Send Them To The Cloud For Running The Search Operation. These Search Tokens Can Be Used To Extract All The Ciphertexts Which Are Produced At Any Time And Contain The Corresponding Keyword. Since This May Lead To Some Information Leakage, It Is More Secure To Propose A Scheme In Which The Search Tokens Can Only Extract The Ciphertexts Generated In A Specified Time Interval. To This End, In This Paper, We Introduce A New Cryptographic Primitive Called Key-policy Attribute-based Temporary Keyword Search (KP-ABTKS) Which Provide This Property. To Evaluate The Security Of Our Scheme, We Formally Prove That Our Proposed Scheme Achieves The Keyword Secrecy Property And Is Secure Against Selectively Chosen Keyword Attack (SCKA) Both In The Random Oracle Model And Under The Hardness Of Decisional Bilinear Diffie-Hellman (DBDH) Assumption. Furthermore, We Show That The Complexity Of The Encryption Algorithm Is Linear With Respect To The Number Of The Involved Attributes. Performance Evaluation Shows Our Scheme's Practicality.

CLOUD RESOURCE SCALING- Cloud Resource Scaling For Time-Bounded And Unbounded Big Data Streaming Applications

Recent Advancements In Technology Have Led To A Deluge Of Big Data Streams That Require Real-time Analysis With Strict Latency Constraints. A Major Challenge, However, Is Determining The Amount Of Resources Required By Applications Processing These Streams Given Their High Volume, Velocity And Variety. The Majority Of Research Efforts On Resource Scaling In The Cloud Are Investigated From The Cloud Provider's Perspective With Little Consideration For Multiple Resource Bottlenecks. We Aim At Analyzing The Resource Scaling Problem From An Application Provider's Point Of View Such That Efficient Scaling Decisions Can Be Made. This Paper Provides Two Contributions To The Study Of Resource Scaling For Big Data Streaming Applications In The Cloud. First, We Present A Layered Multi-dimensional Hidden Markov Model (LMD-HMM) For Managing Time-bounded Streaming Applications. Second, To Cater To Unbounded Streaming Applications, We Propose A Framework Based On A Layered Multi-dimensional Hidden Semi-Markov Model (LMD-HSMM). The Parameters In Our Models Are Evaluated Using Modified Forward And Backward Algorithms. Our Detailed Experimental Evaluation Results Show That LMD-HMM Is Very Effective With Respect To Cloud Resource Prediction For Bounded Streaming Applications Running For Shorter Periods While The LMD-HSMM Accurately Predicts The Resource Usage For Streaming Applications Running For Longer Periods.

ONLINE FINGERPRINT AUTHENTICATION SCHEME- Efficient And Privacy-preserving Online Fingerprint Authentication Scheme Over Outsourced Data

With The Pervasiveness Of Mobile Devices And The Development Of Biometric Technology, Biometric Identification, Which Can Achieve Individual Authentication Relies On Personal Biological Or Behavioral Characteristics, Has Attracted Widely Considerable Interest. However, Privacy Issues Of Biometric Data Bring Out Increasing Concerns Due To The Highly Sensitivity Of Biometric Data. Aiming At This Challenge, In This Paper, We Present A Novel Privacy-preserving Online Fingerprint Authentication Scheme, Named E-Finga, Over Encrypted Outsourced Data. In The Proposed E-Finga Scheme, The User's Fingerprint Registered In Trust Authority Can Be Outsourced To Different Servers With User's Authorization, And Secure, Accurate And Efficient Authentication Service Can Be Provided Without The Leakage Of Fingerprint Information. Specifically, An Improved Homomorphic Encryption Technology For Secure Euclidean Distance Calculation To Achieve An Efficient Online Fingerprint Matching Algorithm Over Encrypted FingerCode Data In The Outsourcing Scenarios. Through Detailed Security Analysis, We Show That E-Finga Can Resist Various Security Threats. In Addition, We Implement E-Finga Over A Workstation With A Real Fingerprint Database, And Extensive Simulation Results Demonstrate That The Proposed E-Finga Scheme Can Serve Efficient And Accurate Online Fingerprint Authentication.

DATA DYNAMICS IN CLOUD STORAGE- Lightweight And Privacy-Preserving Delegatable Proofs Of Storage With Data Dynamics In Cloud Storage

Cloud Storage Has Been In Widespread Use Nowadays, Which Alleviates Users' Burden Of Local Data Storage. Meanwhile, How To Ensure The Security And Integrity Of The Outsourced Data Stored In A Cloud Storage Server Has Also Attracted Enormous Attention From Researchers. Proofs Of Storage (POS) Is The Main Technique Introduced To Address This Problem. Publicly Verifiable POS Allowing A Third Party To Verify The Data Integrity On Behalf Of The Data Owner Significantly Improves The Scalability Of Cloud Service. However, Most Of Existing Publicly Verifiable POS Schemes Are Extremely Slow To Compute Authentication Tags For All Data Blocks Due To Many Expensive Group Exponentiation Operations, Even Much Slower Than Typical Network Uploading Speed, And Thus It Becomes The Bottleneck Of The Setup Phase Of The POS Scheme. In This Article, We Propose A New Variant Formulation Called “Delegatable Proofs Of Storage (DPOS)”. Then, We Construct A Lightweight Privacy-preserving DPOS Scheme, Which On One Side Is As Efficient As Private POS Schemes, And On The Other Side Can Support Third Party Auditor And Can Switch Auditors At Anytime, Close To The Functionalities Of Publicly Verifiable POS Schemes. Compared To Traditional Publicly Verifiable POS Schemes, We Speed Up The Tag Generation Process By At Least Several Hundred Times, Without Sacrificing Efficiency In Any Other Aspect. In Addition, We Extend Our Scheme To Support Fully Dynamic Operations With High Efficiency, Reducing The Computation Of Any Data Update To O(log N) And Simultaneously Only Requiring Constant Communication Costs. We Prove That Our Scheme Is Sound And Privacy Preserving Against Auditor In The Standard Model. Experimental Results Verify The Efficient Performance Of Our Scheme.

PRIVACY PROTECTION AND INTRUSION AVOIDANCE FOR CLOUDLET-BASED MEDICAL DATA SHARING

With The Popularity Of Wearable Devices, Along With The Development Of Clouds And Cloudlet Technology, There Has Been Increasing Need To Provide Better Medical Care. The Processing Chain Of Medical Data Mainly Includes Data Collection, Data Storage And Data Sharing, Etc.

VIRTUAL NETWORK RECOGNITION- Virtual Network Recognition And Optimization In SDN-enabled Cloud Environment

Cloud Computing Is A Scalable And Efficient Technology For Providing Different Services. For Better Reconfigurability And Other Purposes, Users Build Virtual Networks In Cloud Environments. Since Some Applications Bring Heavy Pressure To Cloud Datacenter Networks, It Is Necessary To Recognize And Optimize Virtual Networks With Different Applications. In Some Cloud Environments, Cloud Providers Are Not Allowed To Monitor User Private Information In Cloud Instances. Therefore, In This Paper, We Present A Virtual Network Recognition And Optimization Method To Improve Quality-of-service (QoS) Of Cloud Services. We First Introduce A Community Detection Method To Recognize Virtual Networks From The Cloud Datacenter Network. Then, We Design A Scheduling Strategy By Combining SDN-based Network Management And Instance Placement To Improve The Service-level Agreements (SLA) Fulfillment. Our Experimental Result Shows That We Can Achieve A Recognition Accuracy As High As 80 Percent To Find Out The Virtual Networks, And The Scheduling Strategy Increases The Number Of SLA Fulfilled Virtual Networks.

DATA SHARING SCHEME- Attribute-Based Data Sharing Scheme Revisited In Cloud Computing

Ciphertext-policy Attribute-based Encryption (CP-ABE) Is A Very Promising Encryption Technique For Secure Data Sharing In The Context Of Cloud Computing. Data Owner Is Allowed To Fully Control The Access Policy Associated With His Data Which To Be Shared. However, CP-ABE Is Limited To A Potential Security Risk That Is Known As Key Escrow Problem, Whereby The Secret Keys Of Users Have To Be Issued By A Trusted Key Authority. Besides, Most Of The Existing CP-ABE Schemes Cannot Support Attribute With Arbitrary State. In This Paper, We Revisit Attribute-based Data Sharing Scheme In Order To Solve The Key Escrow Issue But Also Improve The Expressiveness Of Attribute, So That The Resulting Scheme Is More Friendly To Cloud Computing Applications. We Propose An Improved Two-party Key Issuing Protocol That Can Guarantee That Neither Key Authority Nor Cloud Service Provider Can Compromise The Whole Secret Key Of A User Individually. Moreover, We Introduce The Concept Of Attribute With Weight, Being Provided To Enhance The Expression Of Attribute, Which Can Not Only Extend The Expression From Binary To Arbitrary State, But Also Lighten The Complexity Of Access Policy. Therefore, Both Storage Cost And Encryption Complexity For A Ciphertext Are Relieved. The Performance Analysis And The Security Proof Show That The Proposed Scheme Is Able To Achieve Efficient And Secure Data Sharing In Cloud Computing.

ATTRIBUTE BASED KEYWORD SEARCH- Attribute-Based Keyword Search Encryption Scheme With Verifiable Ciphertext Via Blockchain

In Order To Realize The Sharing Of Data By Multiple Users On The Blockchain, This Paper Proposes An Attribute-based Searchable Encryption With Verifiable Ciphertext Scheme Via Blockchain. The Scheme Uses The Public Key Algorithm To Encrypt The Keyword, The Attribute-based Encryption Algorithm To Encrypt The Symmetric Key, And The Symmetric Key To Encrypt The File. The Keyword Index Is Stored On The Blockchain, And The Ciphertext Of The Symmetric Key And File Are Stored On The Cloud Server. The Scheme Uses Searchable Encryption Technology To Achieve Secure Search On The Blockchain, Uses The Immutability Of The Blockchain To Ensure The Security Of The Keyword Ciphertext, Uses Verify Algorithm Guarantees The Integrity Of The Data On The Cloud. When The User's Attributes Need To Be Changed Or The Ciphertext Access Structure Is Changed, The Scheme Uses Proxy Re-encryption Technology To Implement The User's Attribute Revocation, And The Authority Center Is Responsible For The Whole Attribute Revocation Process. The Security Proof Shows That The Scheme Can Achieve Ciphertext Security, Keyword Security And Anti-collusion. In Addition, The Numerical Results Show That The Proposed Scheme Is Effective.

AGGRESSIVE RESOURCE- Aggressive Resource Provisioning For Ensuring QoS In Virtualized Environments

Elasticity Has Now Become The Elemental Feature Of Cloud Computing As It Enables The Ability To Dynamically Add Or Remove Virtual Machine Instances When Workload Changes. However, Effective Virtualized Resource Management Is Still One Of The Most Challenging Tasks. When The Workload Of A Service Increases Rapidly, Existing Approaches Cannot Respond To The Growing Performance Requirement Efficiently Because Of Either Inaccuracy Of Adaptation Decisions Or The Slow Process Of Adjustments, Both Of Which May Result In Insufficient Resource Provisioning. As A Consequence, The Quality Of Service (QoS) Of The Hosted Applications May Degrade And The Service Level Objective (SLO) Will Be Thus Violated. In This Paper, We Introduce SPRNT, A Novel Resource Management Framework, To Ensure High-level QoS In The Cloud Computing System. SPRNT Utilizes An Aggressive Resource Provisioning Strategy Which Encourages SPRNT To Substantially Increase The Resource Allocation In Each Adaptation Cycle When Workload Increases. This Strategy First Provisions Resources Which Are Possibly More Than Actual Demands, And Then Reduces The Over-provisioned Resources If Needed. By Applying The Aggressive Strategy, SPRNT Can Satisfy The Increasing Performance Requirement In The First Place So That The QoS Can Be Kept At A High Level. The Experimental Results Show That SPRNT Achieves Up To 7.7× Speedup In Adaptation Time, Compared With Existing Efforts. By Enabling Quick Adaptation, SPRNT Limits The SLO Violation Rate Up To 1.3 Percent Even When Dealing With Rapidly Increasing Workload.

STRATEGIC MANAGEMENT OF CLOUD COMPUTING SERVICES- Focusing On Consumer Adoption Behavior

The Emergence Of Cloud Computing Services Has Led To An Increased Interest In The Technology Among The General Public And Enterprises Marketing These Services. Although There Is A Need For Studies With A Managerial Relevance For This Emerging Market, The Lack Of Market Analysis Hampers Such Investigations. Therefore, This Study Focuses On The End-user Market For Cloud Computing In Korea. We Conduct A Quantitative Analysis To Show Consumer Adoption Behavior For These Services, Particularly Infrastructure As A Service (IaaS). Bayesian Mixed Logit Model And The Multivariate Probit Model Are Used To Analyze The Data Collected By A Conjoint Survey. From This Analysis, We Find That The Service Fee And Stability Are The Most Critical Adoption Factors. We Also Present An Analysis On The Relationship Between Terminal Devices And IaaS, Classified By Core Attributes Such As Price, Stability, And Storage Capacity. From These Relationships, We Find That Larger Storage Capacity Is More Important For Mobile Devices Such As Laptops Than Desktops. Based On The Results Of The Analysis, This Study Also Recommends Useful Strategies To Enable Enterprise Managers To Focus On More Appropriate Service Attributes, And To Target Suitable Terminal Device Markets Matching The Features Of The Service

REVOCABLE ATTRIBUTE-BASED ENCRYPTION- Revocable Attribute-Based Encryption With Data Integrity In Clouds

Cloud Computing Enables Enterprises And Individu-1 Als To Outsource And Share Their Data. This Way, Cloud Computing 2 Eliminates The Heavy Workload Of Local Information Infrastruc-3 Ture. Attribute-based Encryption Has Become A Promising Solution 4 For Encrypted Data Access Control In Clouds Due To The Ability 5 To Achieve One-to-many Encrypted Data Sharing. Revocation Is A 6 Critical Requirement For Encrypted Data Access Control Systems. 7 After Outsourcing The Encrypted Attribute-based Ciphertext To The 8 Cloud, The Data Owner May Want To Revoke Some Recipients That 9 Were Authorized Previously, Which Means That The Outsourced 10 Attribute-based Ciphertext Needs To Be Updated To A New One 11 That Is Under The Revoked Policy. The Integrity Issue Arises When 12 The Revocation Is Executed. When A New Ciphertext With The 13 Revoked Access Policy Is Generated By The Cloud Server, The Data 14 Recipient Cannot Be Sure That The Newly Generated Ciphertext 15 Guarantees To Be Decrypted To The Same Plaintext As The Originally 16 Encrypted Data, Since The Cloud Server Is Provided By A Third 17 Party, Which Is Not Fully Trusted. In This Paper, We Consider 18 A New Security Requirement For The Revocable Attribute-based 19 Encryption Schemes: Integrity. We Introduce A Formal Definition 20 And Security Model For The Revocable Attribute-based Encryption 21 With Data Integrity Protection (RABE-DI). Then, We Propose 22 A Concrete RABE-DI Scheme And Prove Its Confidentiality And 23 Integrity Under The Defined Security Model. Finally, We Present 24 An Implementation Result And Provide Performance Evaluation 25 Which Shows That Our Scheme Is Efficient And Practical.

CLOUD STORAGE USING DECENTRALIZED BLOCKCHAIN- Enhancing Security Of Data In Cloud Storage Using Decentralized Blockchain

Nowadays, Large Amount Of Data Is Stored On The Cloud Which Is Required To Be Protected From The Unauthorized Users. To Maintain The Privacy And Security Of Data Various Algorithms Are Used. The Objective Of Every System Is To Achieve Confidentiality, Integrity, Availability (CIA). However, The Existing Centralized Cloud Storage Lacks To Provide These CIA Properties. So, To Enhance The Security Of Data And Storing Techniques, Decentralized Cloud Storage Is Used Along With Blockchain Technology. It Effectively Helps To Protect Data From Tampering Or Deleting A Part Of Data. The Data Stored In Blockchain Is Linked To Each Other By A Chain Of Blocks. Each Block Has Its Hash Value, Which Is Stored In Next Block. Thus It Reduces The Chances Of Data Altering. For This Purpose, SHA-512 Hashing Algorithm Is Used. Hashing Algorithm Is Used In Many Aspects, Where The Security Of Data Is Required Such As Message Digest, Password Verification, Digital Certificates And In Blockchain. By The Combination Of These Methods And Algorithms, Data Becomes More Secure And Reliable. However, With The Help Of Various Algorithms, The Security Of The Data Can Be Enhanced. Also, Advance Encryption Standard (AES) Is Used To Encrypt And Decrypt The Data Due To The Significant Features Of This Algorithm.

SECURE MULTI-KEYWORD FUZZY SEARCHES- Secure Multi-Keyword Fuzzy Searches With Enhanced Service Quality In Cloud Computing

With The Ever-increasing Amount Of Data Resided In A Cloud, How To Provide Users With Secure And Practical Query Services Has Become The Key To Improve The Quality Of Cloud Services. Fuzzy Searchable Encryption (FSE) Is Identified As One Of The Most Promising Approaches For Enabling Secure Query Services, Since It Allows Searching Encrypted Data By Using Keywords With Spelling Errors. However, Existing FSE Schemes Are Far From The Practical Use For The Following Reasons: (1) Inflexibility. It Is Hard For Them To Simultaneously Support AND And OR Semantics In A Multi-keyword Query. (2) Inefficiency. They Require Sequentially Scanning A Whole Dataset To Find Matched Files, And Thus Are Difficult To Apply To A Large-scale Dataset. (3) Limited Robustness. It Is Difficult For Them To Resist The Linear Analysis Attack In The Known-background Model. To Fix The Above Problems, This Article Proposes Matrix-based Multi-keyword Fuzzy Search (M2FS) Schemes, Which Support Approximate Keyword Matching By Exploiting The Indecomposable Property Of Primes. Specifically, We First Present A Basic Scheme, Called M2FS-B, Where Multiple Keywords In A Query Or A File Are Constructed As Prime-related Matrices Such That The Result Of Matrix Multiplication Can Be Employed To Determine The Level Of Matching For Different Query Semantics. Then, We Construct An Advanced Scheme, Named M2FS-E, Which Builds A Searchable Index As A Keyword Balanced Binary (KBB) Tree For Dynamic And Parallel Searches, While Adding Random Noises Into A Query Matrix For Enhanced Robustness. Extensive Analyses And Experiments Demonstrate The Validity Of Our M2FS Schemes.

CLOUD AUCTION- A Two-Stage Auction Mechanism For Cloud Resource Allocation

The Contemporary Literature On Cloud Resource Allocation Is Mostly Focused On Studying The Interactions Between Customers And Cloud Managers. Nevertheless, The Recent Growth In The Customers’ Demands And The Emergence Of Private Cloud Providers (CPs) Entice The Cloud Managers To Rent Extra Resources From The CPs So As To Handle Their Backlogged Tasks And Attract More Customers. This Also Renders The Interactions Between The Cloud Managers And The CPs An Important Problem To Study. In This Paper, We Investigate Both Interactions Through A Two-stage Auction Mechanism. For The Interactions Between Customers And Cloud Managers, We Adopt The Options-based Sequential Auctions (OBSAs) To Design The Cloud Resource Allocation Paradigm. As Compared To Existing Works, Our Framework Can Handle Customers With Heterogeneous Demands, Provide Truthfulness As The Dominant Strategy, Enjoy A Simple Winner Determination Procedure, And Preclude The Delayed Entrance Issue. We Also Provide The Performance Analysis Of The OBSAs, Which Is Among The First In Literature. Regarding The Interactions Between Cloud Managers And CPs, We Propose Two Parallel Markets For Resource Gathering, And Capture The Selfishness Of The CPs By Their Offered Prices . We Conduct A Comprehensive Analysis Of The Two Markets And Identify The Bidding Strategies Of The Cloud Managers.

DATA OWNER SIDE AND CLOUD SIDE- Combining Data Owner-Side And Cloud-Side Access Control For Encrypted Cloud Storage

People Endorse The Great Power Of Cloud Computing, But Cannot Fully Trust The Cloud Providers To Host Privacy-sensitive Data, Due To The Absence Of User-to-cloud Controllability. To Ensure Confidentiality, Data Owners Outsource Encrypted Data Instead Of Plaintexts. To Share The Encrypted Files With Other Users, Ciphertext-policy Attribute-based Encryption (CP-ABE) Can Be Utilized To Conduct Fine-grained And Owner-centric Access Control. But This Does Not Sufficiently Become Secure Against Other Attacks. Many Previous Schemes Did Not Grant The Cloud Provider The Capability To Verify Whether A Downloader Can Decrypt. Therefore, These Files Should Be Available To Everyone Accessible To The Cloud Storage. A Malicious Attacker Can Download Thousands Of Files To Launch Economic Denial Of Sustainability (EDoS) Attacks, Which Will Largely Consume The Cloud Resource. The Payer Of The Cloud Service Bears The Expense. Besides, The Cloud Provider Serves Both As The Accountant And The Payee Of Resource Consumption Fee, Lacking The Transparency To Data Owners. These Concerns Should Be Resolved In Real-world Public Cloud Storage. In This Paper, We Propose A Solution To Secure Encrypted Cloud Storages From EDoS Attacks And Provide Resource Consumption Accountability. It Uses CP-ABE Schemes In A Black-box Manner And Complies With Arbitrary Access Policy Of The CP-ABE. We Present Two Protocols For Different Settings, Followed By Performance And Security Analysis.

CLOUD SMART HEALTHCARE SYSTEM- A Smart-Contract-Based Access Control Framework For Cloud Smart Healthcare System

In Current Healthcare Systems, Electronic Medical Records (EMRs) Are Always Located In Different Hospitals And Controlled By A Centralized Cloud Provider. However, It Leads To Single Point Of Failure As Patients Being The Real Owner Lose Track Of Their Private And Sensitive EMRs. Hence, This Article Aims To Build An Access Control Framework Based On Smart Contract, Which Is Built On The Top Of Distributed Ledger (blockchain), To Secure The Sharing Of EMRs Among Different Entities Involved In The Smart Healthcare System. For This, We Propose Four Forms Of Smart Contracts For User Verification, Access Authorization, Misbehavior Detection, And Access Revocation, Respectively. In This Framework, Considering The Block Size Of Ledger And Huge Amount Of Patient Data, The EMRs Are Stored In Cloud After Being Encrypted Through The Cryptographic Functions Of Elliptic Curve Cryptography (ECC) And Edwards-curve Digital Signature Algorithm (EdDSA), While Their Corresponding Hashes Are Packed Into Blockchain. The Performance Evaluation Based On A Private Ethereum System Is Used To Verify The Efficiency Of Proposed Access Control Framework In The Real-time Smart Healthcare System.

CLOUD RESOURCE ALLOCATION- Dynamic Cloud Resource Allocation Considering Demand Uncertainty

Cloud Computing Provisions Scalable Resources For High Performance Industrial Applications. Cloud Providers Usually Offer Two Types Of Usage Plans: Reserved And On-demand. Reserved Plans Offer Cheaper Resources For Long-term Contracts While On-demand Plans Are Available For Short Or Long Periods But Are More Expensive. To Satisfy Incoming User Demands With Reasonable Costs, Cloud Resources Should Be Allocated Efficiently. Most Existing Works Focus On Either Cheaper Solutions With Reserved Resources That May Lead To Under-provisioning Or Over-provisioning, Or Costly Solutions With On-demand Resources. Since Inefficiency Of Allocating Cloud Resources Can Cause Huge Provisioning Costs And Fluctuation In Cloud Demand, Resource Allocation Becomes A Highly Challenging Problem. In This Paper, We Propose A Hybrid Method To Allocate Cloud Resources According To The Dynamic User Demands. This Method Is Developed As A Two-phase Algorithm That Consists Of Reservation And Dynamic Provision Phases. In This Way, We Minimize The Total Deployment Cost By Formulating Each Phase As An Optimization Problem While Satisfying Quality Of Service. Due To The Uncertain Nature Of Cloud Demands, We Develop A Stochastic Optimization Approach By Modeling User Demands As Random Variables. Our Algorithm Is Evaluated Using Different Experiments And The Results Show Its Efficiency In Dynamically Allocating Cloud Resources.

SECURE KEY DISTRIBUTION MANAGEMENT- Efficient Provable Of Secure Key Distribution Management

In Distribute The Key To Both Sender And Receiver To Avoid The Hacking Of Keys. So This Architecture Will Provide High Level Security. This Work Presents Key Distribution To Safeguard High Level Security In Large Networks, New Directions In Classical Cryptography And Symmetric Cryptography. Two Three-party Key Distributions, One With Implicit User Authentication And The Other With Explicit Trusted Centers’ Authentication, Are Proposed To Demonstrate The Merits Of The New Combination. The Project Titled “Efficient Provable Of Secure Key Distribution Management” Is Designed Using Microsoft Visual Studio.Net 2005 As Front End And Microsoft SQL Server 2000 As Back End Which Works In .Net Framework Version 2.0. The Coding Language Used Is C# .Net. We Authenticated Three Parties Into This Project.

SECURE DATA FORWARDING- A Secure Erasure Code-Based Cloud Storage System With Secure Data Forwarding

A Cloud Storage System, Consisting Of A Collection Of Storage Servers, Provides Long-term Storage Services Over The Internet. Storing Data In A Third Party's Cloud System Causes Serious Concern Over Data Confidentiality. General Encryption Schemes Protect Data Confidentiality, But Also Limit The Functionality Of The Storage System Because A Few Operations Are Supported Over Encrypted Data. Constructing A Secure Storage System That Supports Multiple Functions Is Challenging When The Storage System Is Distributed And Has No Central Authority. We Propose A Threshold Proxy Re-encryption Scheme And Integrate It With A Decentralized Erasure Code Such That A Secure Distributed Storage System Is Formulated. The Distributed Storage System Not Only Supports Secure And Robust Data Storage And Retrieval, But Also Lets A User Forward His Data In The Storage Servers To Another User Without Retrieving The Data Back. The Main Technical Contribution Is That The Proxy Re-encryption Scheme Supports Encoding Operations Over Encrypted Messages As Well As Forwarding Operations Over Encoded And Encrypted Messages. Our Method Fully Integrates Encrypting, Encoding, And Forwarding. We Analyze And Suggest Suitable Parameters For The Number Of Copies Of A Message Dispatched To Storage Servers And The Number Of Storage Servers Queried By A Key Server. These Parameters Allow More Flexible Adjustment Between The Number Of Storage Servers And Robustness.

MULTI OWNER DATA SHARING- MONA-Secure Multi-Owner Data Sharing For Dynamic Groups In The Cloud

With The Character Of Low Maintenance, Cloud Computing Provides An Economical And Efficient Solution For Sharing Group Resource Among Cloud Users. Unfortunately, Sharing Data In A Multi-owner Manner While Preserving Data And Identity Privacy From An Untrusted Cloud Is Still A Challenging Issue, Due To The Frequent Change Of The Membership. In This Paper, We Propose A Secure Multi-owner Data Sharing Scheme, Named Mona, For Dynamic Groups In The Cloud. By Leveraging Group Signature And Dynamic Broadcast Encryption Techniques, Any Cloud User Can Anonymously Share Data With Others. Meanwhile, The Storage Overhead And Encryption Computation Cost Of Our Scheme Are Independent With The Number Of Revoked Users. In Addition, We Analyze The Security Of Our Scheme With Rigorous Proofs, And Demonstrate The Efficiency Of Our Scheme In Experiments.

SECURE SHARING OF PERSONAL RECORD- Scalable And Secure Sharing Of Personal Health Records In Cloud Computing Using Attribute-Based Encryption

Personal Health Record (PHR) Is An Emerging Patient-centric Model Of Health Information Exchange, Which Is Often Outsourced To Be Stored At A Third Party, Such As Cloud Providers. However, There Have Been Wide Privacy Concerns As Personal Health Information Could Be Exposed To Those Third Party Servers And To Unauthorized Parties. To Assure The Patients' Control Over Access To Their Own PHRs, It Is A Promising Method To Encrypt The PHRs Before Outsourcing. Yet, Issues Such As Risks Of Privacy Exposure, Scalability In Key Management, Flexible Access, And Efficient User Revocation, Have Remained The Most Important Challenges Toward Achieving Fine-grained, Cryptographically Enforced Data Access Control. In This Paper, We Propose A Novel Patient-centric Framework And A Suite Of Mechanisms For Data Access Control To PHRs Stored In Semitrusted Servers. To Achieve Fine-grained And Scalable Data Access Control For PHRs, We Leverage Attribute-based Encryption (ABE) Techniques To Encrypt Each Patient's PHR File. Different From Previous Works In Secure Data Outsourcing, We Focus On The Multiple Data Owner Scenario, And Divide The Users In The PHR System Into Multiple Security Domains That Greatly Reduces The Key Management Complexity For Owners And Users. A High Degree Of Patient Privacy Is Guaranteed Simultaneously By Exploiting Multiauthority ABE. Our Scheme Also Enables Dynamic Modification Of Access Policies Or File Attributes, Supports Efficient On-demand User/attribute Revocation And Break-glass Access Under Emergency Scenarios. Extensive Analytical And Experimental Results Are Presented Which Show The Security, Scalability, And Efficiency Of Our Proposed Scheme.

VISUAL AUTHENTICATION- Key Logging-Resistant Visual Authentication Protocols

The Design Of Secure Authentication Protocols Is Quite Challenging, Considering That Various Kinds Of Root Kits Reside In Personal Computers (PCs) To Observe User's Behavior And To Make PCs Untrusted Devices. Involving Human In Authentication Protocols, While Promising, Is Not Easy Because Of Their Limited Capability Of Computation And Memorization. Therefore, Relying On Users To Enhance Security Necessarily Degrades The Usability. On The Other Hand, Relaxing Assumptions And Rigorous Security Design To Improve The User Experience Can Lead To Security Breaches That Can Harm The Users' Trust. In This Paper, We Demonstrate How Careful Visualization Design Can Enhance Not Only The Security But Also The Usability Of Authentication. To That End, We Propose Two Visual Authentication Protocols: One Is A One-time-password Protocol, And The Other Is A Password-based Authentication Protocol. Through Rigorous Analysis, We Verify That Our Protocols Are Immune To Many Of The Challenging Authentication Attacks Applicable In The Literature. Furthermore, Using An Extensive Case Study On A Prototype Of Our Protocols, We Highlight The Potential Of Our Approach For Real-world Deployment: We Were Able To Achieve A High Level Of Usability While Satisfying Stringent Security Requirements.

TRACING AND REVOCATION FOR ENCRYTPTED FILE- An Efficient Traitor Tracing And Revocation For Encrypted File Syncing-and-Sharing In Cloud

Recently, Many Enterprises Have Moved Their Data Into The Cloud By Using File Syncing And Sharing (FSS) Services, Which Have Been Deployed For Mobile Users. However, Bring-Your-Own-Device (BYOD) Solutions For Increasingly Deployed Mobile Devices Have Also In Fact Raised A New Challenge For How To Prevent Users From Abusing The FSS Service. In This Paper, We Address This Issue By Using A New System Model Involving Anomaly Detection, Tracing, And Revocation Approaches. The Presented Solution Applies A New Threshold Public Key Based Cryptosystem, Called Partially-ordered Hierarchical Encryption (PHE), Which Implements A Partial-order Key Hierarchy And It Is Similar To Role Hierarchy Widely Used In RBAC. PHE Provides Two Main Security Mechanisms, I.e., Traitor Tracing And Key Revocation, Which Can Greatly Improve The Efficiency Compared To Previous Approaches. The Security And Performance Analysis Shows That PHE Is A Provably Secure Threshold Encryption And Provides Following Salient Management And Performance Benefits: It Can Promise To Efficiently Trace All Possible Traitor Coalitions And Support Public Revocation Not Only For The Users But For The Specified Groups.

DATA SECURITY BASED ON ARTIFICIAL IMMUNE ALGORITHM- Data Security Strategy Based On Artificial Immune Algorithm For Cloud Computing

With The Fast Development Of Cloud Computing And Its Wide Application, Data Security Plays An Important Role In Cloud Computing. This Paper Brought Up A Novel Data Security Strategy Based On Artificial Immune Algorithm On Architecture Of HDFS For Cloud Computing. Firstly, We Explained The Main Factors Influence Data Security In Cloud Environment. Then We Introduce HDFS Architecture, Data Security Model And Put Forward An Improved Security Model For Cloud Computing. In The Third Section, Artificial Immune Algorithm Related With Negative Selection And Dynamic Selection Algorithm That Adopted In Our System And How They Applied To Cloud Computing Are Depicted In Detail. Finally Simulations Are Taken By Two Steps. Former Simulations Are Carried Out To Prove The Performance Of Artificial Immune Algorithm Brought Up In This Paper, The Latter Simulation Are Running On Cloudsim Platform To Testify That Data Security Strategy Based On Artificial Immune Algorithm For Cloud Computing Is Efficient.

HOMOMORPHIC AUTHENTICATION- Holomorphic Authentication With Random Masking Technique Ensuring Privacy Security In Cloud Computing

Cloud Computing May Be Defined As Delivery Of Product Rather Than Service. Cloud Computing Is A Internet Based Computing Which Enables Sharing Of Services. Many Users Place Their Data In The Cloud. However, The Fact That Users No Longer Have Physical Possession Of The Possibly Large Size Of Outsourced Data Makes The Data Integrity Protection In Cloud Computing A Very Challenging And Potentially Formidable Task, Especially For Users With Constrained Computing Resources And Capabilities. So Correctness Of Data And Security Is A Prime Concern. This Article Studies The Problem Of Ensuring The Integrity And Security Of Data Storage In Cloud Computing. Security In Cloud Is Achieved By Signing The Data Block Before Sending To The Cloud. Signing Is Performed Using Boneh–Lynn–Shacham (BLS) Algorithm Which Is More Secure Compared To Other Algorithms. To Ensure The Correctness Of Data, We Consider An External Auditor Called As Third Party Auditor (TPA), On Behalf Of The Cloud User, To Verify The Integrity Of The Data Stored In The Cloud. By Utilizing Public Key Based Homomorphic Authenticator With Random Masking Privacy Preserving Public Auditing Can Be Achieved. The Technique Of Bilinear Aggregate Signature Is Used To Achieve Batch Auditing. Batch Auditing Reduces The Computation Overhead. Extensive Security And Performance Analysis Shows The Proposed Schemes Are Provably Secure And Highly Efficient.

Server Manipulation Detection On Cloud

Remote Data Trustworthiness Checking Is A Fundamental Advancement In Dispersed Computing. In Recent Times Numerous Works Center Around Providing Data Elements As Well As Open Proof To This Kind Of Conventions. Existing Conventions Can Support The Two Features With The Help Of An Untouchable Evaluator. In A Earlier Work, Propose A Weak Information Honesty Checking Convention That Bolsters Information Elements. Right Now, Adjust To Help Open Undeniable Nature. The Proposed Show Reinforces Open Evident Nature Without Help Of A Pariah Examiner. What's More, The Proposed Way Doesn't Reveal Any Personal Data To Third Party Verifiers. Using A Conventional Investigation, We Show The Precision And Security Of The Show. Starting There Forward, Through Debatable Investigation And Exploratory Results, We Show That The Proposed Show Has A Conventional Presentation.

DYNAMIC OWNERSHIP MANAGEMENT- Secure Data Deduplication With Dynamic Ownership Management In Cloud Storage

In Cloud Storage Services, Deduplication Technology Is Commonly Used To Reduce The Space And Bandwidth Requirements Of Services By Eliminating Redundant Data And Storing Only A Single Copy Of Them. Deduplication Is Most Effective When Multiple Users Outsource The Same Data To The Cloud Storage, But It Raises Issues Relating To Security And Ownership. Proof-of-ownership Schemes Allow Any Owner Of The Same Data To Prove To The Cloud Storage Server That He Owns The Data In A Robust Way. However, Many Users Are Likely To Encrypt Their Data Before Outsourcing Them To The Cloud Storage To Preserve Privacy, But This Hampers Deduplication Because Of The Randomization Property Of Encryption. Recently, Several Deduplication Schemes Have Been Proposed To Solve This Problem By Allowing Each Owner To Share The Same Encryption Key For The Same Data. However, Most Of The Schemes Suffer From Security Flaws, Since They Do Not Consider The Dynamic Changes In The Ownership Of Outsourced Data That Occur Frequently In A Practical Cloud Storage Service. In This Paper, We Propose A Novel Server-side Deduplication Scheme For Encrypted Data. It Allows The Cloud Server To Control Access To Outsourced Data Even When The Ownership Changes Dynamically By Exploiting Randomized Convergent Encryption And Secure Ownership Group Key Distribution. This Prevents Data Leakage Not Only To Revoked Users Even Though They Previously Owned That Data, But Also To An Honest-but-curious Cloud Storage Server. In Addition, The Proposed Scheme Guarantees Data Integrity Against Any Tag Inconsistency Attack. Thus, Security Is Enhanced In The Proposed Scheme. The Efficiency Analysis Results Demonstrate That The Proposed Scheme Is Almost As Efficient As The Previous Schemes, While The Additional Computational Overhead Is Negligible.

SECURITY GUARANTEES IN PUBLIC CLOUD- Providing User Security Guarantees In Public Infrastructure Clouds

The Infrastructure Cloud (IaaS) Service Model Offers Improved Resource Flexibility And Availability, Where Tenants - Insulated From The Minutiae Of Hardware Maintenance - Rent Computing Resources To Deploy And Operate Complex Systems. Large-scale Services Running On IaaS Platforms Demonstrate The Viability Of This Model; Nevertheless, Many Organizations Operating On Sensitive Data Avoid Migrating Operations To IaaS Platforms Due To Security Concerns. In This Paper, We Describe A Framework For Data And Operation Security In IaaS, Consisting Of Protocols For A Trusted Launch Of Virtual Machines And Domain-based Storage Protection. We Continue With An Extensive Theoretical Analysis With Proofs About Protocol Resistance Against Attacks In The Defined Threat Model. The Protocols Allow Trust To Be Established By Remotely Attesting Host Platform Configuration Prior To Launching Guest Virtual Machines And Ensure Confidentiality Of Data In Remote Storage, With Encryption Keys Maintained Outside Of The IaaS Domain. Presented Experimental Results Demonstrate The Validity And Efficiency Of The Proposed Protocols. The Framework Prototype Was Implemented On A Test Bed Operating A Public Electronic Health Record System, Showing That The Proposed Protocols Can Be Integrated Into Existing Cloud Environments.

MUITIMEDIA CONTENT PROTECTION SYSTEM- Cloud-Based Multimedia Content Protection System

We Propose A New Design For Large-scale Multimedia Content Protection Systems. Our Design Leverages Cloud Infrastructures To Provide Cost Efficiency, Rapid Deployment, Scalability, And Elasticity To Accommodate Varying Workloads. The Proposed System Can Be Used To Protect Different Multimedia Content Types, Including Videos, Images, Audio Clips, Songs, And Music Clips. The System Can Be Deployed On Private And/or Public Clouds. Our System Has Two Novel Components: (i) Method To Create Signatures Of Videos, And (ii) Distributed Matching Engine For Multimedia Objects. The Signature Method Creates Robust And Representative Signatures Of Videos That Capture The Depth Signals In These Videos And It Is Computationally Efficient To Compute And Compare As Well As It Requires Small Storage. The Distributed Matching Engine Achieves High Scalability And It Is Designed To Support Different Multimedia Objects. We Implemented The Proposed System And Deployed It On Two Clouds: Amazon Cloud And Our Private Cloud. Our Experiments With More Than 11,000 Videos And 1 Million Images Show The High Accuracy And Scalability Of The Proposed System. In Addition, We Compared Our System To The Protection System Used By YouTube And Our Results Show That The YouTube Protection System Fails To Detect Most Copies Of Videos, While Our System Detects More Than 98% Of Them.

RESOURCE MATCHMAKIG ACROSS MULTIPLE CLOUDS- Service Operator-Aware Trust Scheme For Resource Matchmaking Across Multiple Clouds

This Paper Proposes A Service Operator-aware Trust Scheme (SOTS) For Resource Matchmaking Across Multiple Clouds. Through Analyzing The Built-in Relationship Between The Users, The Broker, And The Service Resources, This Paper Proposes A Middleware Framework Of Trust Management That Can Effectively Reduces User Burden And Improve System Dependability. Based On Multidimensional Resource Service Operators, We Model The Problem Of Trust Evaluation As A Process Of Multi-attribute Decision-making, And Develop An Adaptive Trust Evaluation Approach Based On Information Entropy Theory. This Adaptive Approach Can Overcome The Limitations Of Traditional Trust Schemes, Whereby The Trusted Operators Are Weighted Manually Or Subjectively. As A Result, Using SOTS, The Broker Can Efficiently And Accurately Prepare The Most Trusted Resources In Advance, And Thus Provide More Dependable Resources To Users. Our Experiments Yield Interesting And Meaningful Observations That Can Facilitate The Effective Utilization Of SOTS In A Large-scale Multi-cloud Environment.

SECURE AUTHORIZED DEDUPLICATION- A Hybrid Cloud Approach For Secure Authorized Deduplication

Data Deduplication Is One Of Important Data Compression Techniques For Eliminating Duplicate Copies Of Repeating Data, And Has Been Widely Used In Cloud Storage To Reduce The Amount Of Storage Space And Save Bandwidth. To Protect The Confidentiality Of Sensitive Data While Supporting Deduplication, The Convergent Encryption Technique Has Been Proposed To Encrypt The Data Before Outsourcing. To Better Protect Data Security, This Paper Makes The First Attempt To Formally Address The Problem Of Authorized Data Deduplication. Different From Traditional Deduplication Systems, The Differential Privileges Of Users Are Further Considered In Duplicate Check Besides The Data Itself. We Also Present Several New Deduplication Constructions Supporting Authorized Duplicate Check In A Hybrid Cloud Architecture. Security Analysis Demonstrates That Our Scheme Is Secure In Terms Of The Definitions Specified In The Proposed Security Model. As A Proof Of Concept, We Implement A Prototype Of Our Proposed Authorized Duplicate Check Scheme And Conduct Testbed Experiments Using Our Prototype. We Show That Our Proposed Authorized Duplicate Check Scheme Incurs Minimal Overhead Compared To Normal Operations.

CLOUD FIREWALL FRAMEWORK- A Decentralized Cloud Firewall Framework With Resources Provisioning Cost Optimization

Cloud Computing Is Becoming Popular As The Next Infrastructure Of Computing Platform. Despite The Promising Model And Hype Surrounding, Security Has Become The Major Concern That People Hesitate To Transfer Their Applications To Clouds. Concretely, Cloud Platform Is Under Numerous Attacks. As A Result, It Is Definitely Expected To Establish A Firewall To Protect Cloud From These Attacks. However, Setting Up A Centralized Firewall For A Whole Cloud Data Center Is Infeasible From Both Performance And Financial Aspects. In This Paper, We Propose A Decentralized Cloud Firewall Framework For Individual Cloud Customers. We Investigate How To Dynamically Allocate Resources To Optimize Resources Provisioning Cost, While Satisfying QoS Requirement Specified By Individual Customers Simultaneously. Moreover, We Establish Novel Queuing Theory Based Model M/Geo/1 And M/Geo/m For Quantitative System Analysis, Where The Service Times Follow A Geometric Distribution. By Employing Z-transform And Embedded Markov Chain Techniques, We Obtain A Closed-form Expression Of Mean Packet Response Time. Through Extensive Simulations And Experiments, We Conclude That An M/Geo/1 Model Reflects The Cloud Firewall Real System Much Better Than A Traditional M/M/1 Model. Our Numerical Results Also Indicate That We Are Able To Set Up Cloud Firewall With Affordable Cost To Cloud Customers.

DOS ATTACK DETECTION - A System For Denial-Of-Service Attack Detection Based On Multivariate Correlation Analysis

Interconnected Systems, Such As Web Servers, Database Servers, Cloud Computing Servers And So On, Are Now Under Threads From Network Attackers. As One Of Most Common And Aggressive Means, Denial-of-service (DoS) Attacks Cause Serious Impact On These Computing Systems. In This Paper, We Present A DoS Attack Detection System That Uses Multivariate Correlation Analysis (MCA) For Accurate Network Traffic Characterization By Extracting The Geometrical Correlations Between Network Traffic Features. Our MCA-based DoS Attack Detection System Employs The Principle Of Anomaly Based Detection In Attack Recognition. This Makes Our Solution Capable Of Detecting Known And Unknown DoS Attacks Effectively By Learning The Patterns Of Legitimate Network Traffic Only. Furthermore, A Triangle-area-based Technique Is Proposed To Enhance And To Speed Up The Process Of MCA. The Effectiveness Of Our Proposed Detection System Is Evaluated Using KDD Cup 99 Data Set, And The Influences Of Both Non-normalized Data And Normalized Data On The Performance Of The Proposed Detection System Are Examined. The Results Show That Our System Outperforms Two Other Previously Developed State-of-the-art Approaches In Terms Of Detection Accuracy.

PRIVACY PRESERVING AUTHENTICATION- Shared Authority Based Privacy-preserving Authentication Protocol In Cloud Computing

Cloud Computing Is An Emerging Data Interactive Paradigm To Realize Users' Data Remotely Stored In An Online Cloud Server. Cloud Services Provide Great Conveniences For The Users To Enjoy The On-demand Cloud Applications Without Considering The Local Infrastructure Limitations. During The Data Accessing, Different Users May Be In A Collaborative Relationship, And Thus Data Sharing Becomes Significant To Achieve Productive Benefits. The Existing Security Solutions Mainly Focus On The Authentication To Realize That A User's Privative Data Cannot Be Illegally Accessed, But Neglect A Subtle Privacy Issue During A User Challenging The Cloud Server To Request Other Users For Data Sharing. The Challenged Access Request Itself May Reveal The User's Privacy No Matter Whether Or Not It Can Obtain The Data Access Permissions. In This Paper, We Propose A Shared Authority Based Privacy-preserving Authentication Protocol (SAPA) To Address Above Privacy Issue For Cloud Storage. In The SAPA, 1) Shared Access Authority Is Achieved By Anonymous Access Request Matching Mechanism With Security And Privacy Considerations (e.g., Authentication, Data Anonymity, User Privacy, And Forward Security); 2) Attribute Based Access Control Is Adopted To Realize That The User Can Only Access Its Own Data Fields; 3) Proxy Re-encryption Is Applied To Provide Data Sharing Among The Multiple Users. Meanwhile, Universal Composability (UC) Model Is Established To Prove That The SAPA Theoretically Has The Design Correctness. It Indicates That The Proposed Protocol Is Attractive For Multi-user Collaborative Cloud Applications.

SERVICES AUDITING- Consistency As A Service Auditing Cloud Consistency

Cloud Storage Services Have Become Commercially Popular Due To Their Overwhelming Advantages. To Provide Ubiquitous Always-on Access, A Cloud Service Provider (CSP) Maintains Multiple Replicas For Each Piece Of Data On Geographically Distributed Servers. A Key Problem Of Using The Replication Technique In Clouds Is That It Is Very Expensive To Achieve Strong Consistency On A Worldwide Scale. In This Paper, We First Present A Novel Consistency As A Service (CaaS) Model, Which Consists Of A Large Data Cloud And Multiple Small Audit Clouds. In The CaaS Model, A Data Cloud Is Maintained By A CSP, And A Group Of Users That Constitute An Audit Cloud Can Verify Whether The Data Cloud Provides The Promised Level Of Consistency Or Not. We Propose A Two-level Auditing Architecture, Which Only Requires A Loosely Synchronized Clock In The Audit Cloud. Then, We Design Algorithms To Quantify The Severity Of Violations With Two Metrics: The Commonality Of Violations, And The Staleness Of The Value Of A Read. Finally, We Devise A Heuristic Auditing Strategy (HAS) To Reveal As Many Violations As Possible. Extensive Experiments Were Performed Using A Combination Of Simulations And Realcloud Deployments To Validate HAS.

QOS PREDICTION IN CLOUD COMPUTING- Exploring Latent Features For Memory-Based QoS Prediction In Cloud Computing

With The Increasing Popularity Of Cloud Computing As A Solution For Building High-quality Applications On Distributed Components, Efficiently Evaluating User-side Quality Of Cloud Components Becomes An Urgent And Crucial Research Problem. However, Invoking All The Available Cloud Components From User-side For Evaluation Purpose Is Expensive And Impractical. To Address This Critical Challenge, We Propose A Neighborhood-based Approach, Called CloudPred, For Collaborative And Personalized Quality Prediction Of Cloud Components. CloudPred Is Enhanced By Feature Modeling On Both Users And Components. Our Approach CloudPred Requires No Additional Invocation Of Cloud Components On Behalf Of The Cloud Application Designers. The Extensive Experimental Results Show That CloudPred Achieves Higher QoS Prediction Accuracy Than Other Competing Methods. We Also Publicly Release Our Large-scale QoS Dataset For Future Related Research In Cloud Computing.

SCALABLE DATA SHARING IN CLOUD Key-Aggregate Cryptosystem For Scalable Data Sharing In Cloud Storage

Data Sharing Is An Important Functionality In Cloud Storage. In This Paper, We Show How To Securely, Efficiently, And Flexibly Share Data With Others In Cloud Storage. We Describe New Public-key Cryptosystems That Produce Constant-size Ciphertexts Such That Efficient Delegation Of Decryption Rights For Any Set Of Ciphertexts Are Possible. The Novelty Is That One Can Aggregate Any Set Of Secret Keys And Make Them As Compact As A Single Key, But Encompassing The Power Of All The Keys Being Aggregated. In Other Words, The Secret Key Holder Can Release A Constant-size Aggregate Key For Flexible Choices Of Ciphertext Set In Cloud Storage, But The Other Encrypted Files Outside The Set Remain Confidential. This Compact Aggregate Key Can Be Conveniently Sent To Others Or Be Stored In A Smart Card With Very Limited Secure Storage. We Provide Formal Security Analysis Of Our Schemes In The Standard Model. We Also Describe Other Application Of Our Schemes. In Particular, Our Schemes Give The First Public-key Patient-controlled Encryption For Flexible Hierarchy, Which Was Yet To Be Known.

RESOURCE MANAGEMENT IN THE CLOUD- Anchor-A Versatile And Efficient Framework For Resource Management In The Cloud

We Present Anchor, A General Resource Management Architecture That Uses The Stable Matching Framework To Decouple Policies From Mechanisms When Mapping Virtual Machines To Physical Servers. In Anchor, Clients And Operators Are Able To Express A Variety Of Distinct Resource Management Policies As They Deem Fit, And These Policies Are Captured As Preferences In The Stable Matching Framework. The Highlight Of Anchor Is A New Many-to-one Stable Matching Theory That Efficiently Matches VMs With Heterogeneous Resource Needs To Servers, Using Both Offline And Online Algorithms. Our Theoretical Analyses Show The Convergence And Optimality Of The Algorithm. Our Experiments With A Prototype Implementation On A 20-node Server Cluster, As Well As Large-scale Simulations Based On Real-world Workload Traces, Demonstrate That The Architecture Is Able To Realize A Diverse Set Of Policy Objectives With Good Performance And Practicality.

CLOUD SECURITY- Cloud Computing- Security Of The Cloud

Cloud Computing Promises To Increase The Velocity With Which Application Are Deployed, Increase Innovation And Lower Costs, All While Increasing Business Agility And Hence Envisioned As The Next Generation Architecture Of IT Enterprise. Nature Of Cloud Computing Builds An Established Trend For Driving Cost Out Of The Delivery Of Services While Increasing The Speed And Agility With Which Services Are Deployed. Cloud Computing Incorporates Virtualization, On Demand Deployment, Internet Delivery Of Services And Open Source Software .From Another Perspective, Everything Is New Because Cloud Computing Changes How We Invent, Develop, Deploy, Scale, Update, Maintain And Pay For Application And The Infrastructure On Which They Run. Because Of These Benefits Of Cloud Computing, It Requires An Effective And Flexible Dynamic Security Scheme To Ensure The Correctness Of Users’ Data In The Cloud. Quality Of Service Is An Important Aspect And Hence, Extensive Cloud Data Security And Performance Is Required.

SECURE MULTI KEYWORD- Towards Secure Multi-Keyword Top-K Retrieval Over Encrypted Cloud Data

Cloud Computing Has Emerging As A Promising Pattern For Data Outsourcing And High-quality Data Services. However, Concerns Of Sensitive Information On Cloud Potentially Causes Privacy Problems. Data Encryption Protects Data Security To Some Extent, But At The Cost Of Compromised Efficiency. Searchable Symmetric Encryption (SSE) Allows Retrieval Of Encrypted Data Over Cloud. In This Paper, We Focus On Addressing Data Privacy Issues Using SSE. For The First Time, We Formulate The Privacy Issue From The Aspect Of Similarity Relevance And Scheme Robustness. We Observe That Server-side Ranking Based On Order-preserving Encryption (OPE) Inevitably Leaks Data Privacy. To Eliminate The Leakage, We Propose A Two-round Searchable Encryption (TRSE) Scheme That Supports Top-(k) Multikeyword Retrieval. In TRSE, We Employ A Vector Space Model And Homomorphic Encryption. The Vector Space Model Helps To Provide Sufficient Search Accuracy, And The Homomorphic Encryption Enables Users To Involve In The Ranking While The Majority Of Computing Work Is Done On The Server Side By Operations Only On Ciphertext. As A Result, Information Leakage Can Be Eliminated And Data Security Is Ensured. Thorough Security And Performance Analysis Show That The Proposed Scheme Guarantees High Security And Practical Efficiency.

HIERARCHICAL ATTRIBUTE- Hasbe - A Hierarchical Attribute Based Solution For Flexible And Scalable Access Control In Cloud Computing

Cloud Computing Has Emerged As One Of The Most Influential Paradigms In The IT Industry In Recent Years. Since This New Computing Technology Requires Users To Entrust Their Valuable Data To Cloud Providers, There Have Been Increasing Security And Privacy Concerns On Outsourced Data. Several Schemes Employing Attribute-based Encryption (ABE) Have Been Proposed For Access Control Of Outsourced Data In Cloud Computing; However, Most Of Them Suffer From Inflexibility In Implementing Complex Access Control Policies. In Order To Realize Scalable, Flexible, And Fine-grained Access Control Of Outsourced Data In Cloud Computing, In This Paper, We Propose Hierarchical Attribute-set-based Encryption (HASBE) By Extending Ciphertext-policy Attribute-set-based Encryption (ASBE) With A Hierarchical Structure Of Users. The Proposed Scheme Not Only Achieves Scalability Due To Its Hierarchical Structure, But Also Inherits Flexibility And Fine-grained Access Control In Supporting Compound Attributes Of ASBE. In Addition, HASBE Employs Multiple Value Assignments For Access Expiration Time To Deal With User Revocation More Efficiently Than Existing Schemes. We Formally Prove The Security Of HASBE Based On Security Of The Ciphertext-policy Attribute-based Encryption (CP-ABE) Scheme By Bethencourt And Analyze Its Performance And Computational Complexity. We Implement Our Scheme And Show That It Is Both Efficient And Flexible In Dealing With Access Control For Outsourced Data In Cloud Computing With Comprehensive Experiments.

SECURE TEXT TRANSFER ON CLOUD- Secure Text Transfer Using Diffie Hellman Key Exchange Based On Cloud

Encryption Is The Technique Of Hiding Private Or Sensitive Information Within Something That Appears To Be Nothing Be A Usual. If A Person Views That Cipher Text, He Or She Will Have No Idea That There Is Any Secret Information. What Encryption Essentially Does Is Exploit Human Perception, Human Senses Are Not Trained To Look For Files That Have Information Inside Of Them. What This System Does Is, It Lets User To Send Text As Secrete Message And Gives A Key Or A Password To Lock The Text, What This Key Does Is, It Encrypts The Text, So That Even If It Is Hacked By Hacker It Will Not Be Able To Read The Text. Receiver Will Need The Key To Decrypt The Hidden Text. User Then Sends The Key To The Receiver And Then He Enters The Key Or Password For Decryption Of Text, He Then Presses Decrypt Key To Get Secret Text From The Sender. Diffie-Hellman Key Exchange Offers The Best Of Both As It Uses Public Key Techniques To Allow The Exchange Of A Private Encryption Key. By Using This Method, You Can Double Ensure That Your Secret Message Is Sent Secretly Without Outside Interference Of Hackers Or Crackers. If Sender Sends This Cipher Text In Public Others Will Not Know What Is It, And It Will Be Received By Receiver. The System Uses Online Database To Store All Related Information. As, The Project Files And A Database File Will Be Stored Into The Azure Cloud, The Project Will Be Accessed In The Web Browser Through Azure Link.

ANTI PHISHING- A Novel Anti Phishing Framework Based On Visual Cryptography

With The Advent Of Internet, Various Online Attacks Has Been Increased And Among Them The Most Popular Attack Is Phishing. Phishing Is An Attempt By An Individual Or A Group To Get Personal Confidential Information Such As Passwords, Credit Card Information From Unsuspecting Victims For Identity Theft, Financial Gain And Other Fraudulent Activities. Fake Websites Which Appear Very Similar To The Original Ones Are Being Hosted To Achieve This. In This Paper We Have Proposed A New Approach Named As "A Novel Anti-phishing Framework Based On Visual Cryptography "to Solve The Problem Of Phishing. Here An Image Based Authentication Using Visual Cryptography Is Implemented. The Use Of Visual Cryptography Is Explored To Preserve The Privacy Of An Image Captcha By Decomposing The Original Image Captcha Into Two Shares (known As Sheets) That Are Stored In Separate Database Servers(one With User And One With Server) Such That The Original Image Captcha Can Be Revealed Only When Both Are Simultaneously Available; The Individual Sheet Images Do Not Reveal The Identity Of The Original Image Captcha. Once The Original Image Captcha Is Revealed To The User It Can Be Used As The Password. Using This Website Cross Verifies Its Identity And Proves That It Is A Genuine Website Before The End Users.

CLOUD DATA AUDITING TECHNIQUES- Cloud Data Auditing Techniques With A Focus On Privacy And Security

Nowadays, Large Amounts Of Data Are Stored With Cloud Service Providers. Third-party Auditors (TPAs), With The Help Of Cryptography, Are Often Used To Verify This Data. However, Most Auditing Schemes Don't Protect Cloud User Data From TPAs. A Review Of The State Of The Art And Research In Cloud Data Auditing Techniques Highlights Integrity And Privacy Challenges, Current Solutions, And Future Research Directions.

DYNAMIC RESOURCE ALLOCATION USING VM- Dynamic Resource Allocation Using Virtual Machines For Cloud Computing Environment

With The Recent Advancement In Cloud Computing Technology, Cloud Computing Allows Users To Upgrade And Downgrade Their Resource Usage Based On Their Needs. Most Of These Benefits Are Achieved From Resource Multiplexing Through Virtualization Technology In The Cloud Model. Using The Virtualization Technology The Data Center Resources Can Be Dynamically Allocated Based On Application Demands. The Concept Of "green Computing" And Skewness Is Introduced To Optimize The Number Of Servers In Use And To Measure The Unevenness In The Multi-dimensional Resource Utilization Of A Server Respectively. By Minimizing Skewness, The Different Types Of Workloads Can Be Combined Effectively And The Overall Utilization Of Server Resources Can Be Improved.

PRIVACY PRESERVING RECORD LINKAGE- Secure Approximate String Matching For Privacy-preserving Record Linkage

Real-world Applications Of Record Linkage Often Require Matching To Be Robust In Spite Of Small Variations In String Fields. For Example, Two Health Care Providers Should Be Able To Detect A Patient In Common, Even If One Record Contains A Typo Or Transcription Error. In The Privacy-preserving Setting, However, The Problem Of Approximate String Matching Has Been Cast As A Trade-off Between Security And Practicality, And The Literature Has Mainly Focused On Bloom Filter Encodings, An Approach Which Can Leak Significant Information About The Underlying Records. We Present A Novel Public-key Construction For Secure Two-party Evaluation Of Threshold Functions In Restricted Domains Based On Embeddings Found In The Message Spaces Of Additively Homomorphic Encryption Schemes. We Use This To Construct An Efficient Two-party Protocol For Privately Computing The Threshold Dice Coefficient. Relative To The Approach Of Bloom Filter Encodings, Our Proposal Offers Formal Security Guarantees And Greater Matching Accuracy. We Implement The Protocol And Demonstrate The Feasibility Of This Approach In Linking Medium-sized Patient Databases With Tens Of Thousands Of Records.

CYBER CIRME TECHINQUES USING DATA MINING- A Data Mining Framework To Predict Cyber Attack For Cyber Security

Cyber-attacks Are Exponentially Increasing Daily With The Advancements Of Technology. Therefore, The Detection And Prediction Of Cyber-attacks Are Very Important For Every Organization That Is Dealing With Sensitive Data For Business Purposes. In This Paper, We Present A Framework On Cyber Security Using A Data Mining Technique To Predict Cyber-attacks That Can Be Helpful To Take Proper Interventions To Reduce The Cyber-attacks. The Two Main Components Of The Framework Are The Detection And Prediction Of Cyber-attacks. The Framework First Extracts The Patterns Related To Cyber-attacks From Historical Data Using A J48 Decision Tree Algorithm And Then Builds A Prediction Model To Predict The Future Cyber-attacks. We Then Apply The Framework On Publicly Available Cyber Security Datasets Provided By The Canadian Institute Of Cybersecurity. In The Datasets, Several Kinds Of Cyber-attacks Are Presented Including DDoS, Port Scan, Bot, Brute Force, SQL Injection, And Heartbleed. The Proposed Framework Correctly Detects The Cyber-attacks And Provides The Patterns Related To Cyber-attacks. The Overall Accuracy Of The Proposed Prediction Model To Detect Cyber-attacks Is Around 99%. The Extracted Patterns Of The Prediction Model On Historical Data Can Be Applied To Predict Any Future Cyber-attacks. The Experimental Results Of The Prediction Model Indicate The Superiority Of The Model To Detect Any Future Cyber-attacks.

FRAUD DETECTION FROM MASSIVE USER BEHAVIORS- Fraud Detection In Dynamic Interaction Network

Fraud Detection From Massive User Behaviors Is Often Regarded As Trying To Find A Needle In A Haystack. In This Paper, We Suggest Abnormal Behavioral Patterns Can Be Better Revealed If Both Sequential And Interaction Behaviors Of Users Can Be Modeled Simultaneously, Which However Has Rarely Been Addressed In Prior Work. Along This Line, We Propose A COllective Sequence And INteraction (COSIN) Model, In Which The Behavioral Sequences And Interactions Between Source And Target Users In A Dynamic Interaction Network Are Modeled Uniformly In A Probabilistic Graphical Model. More Specifically, The Sequential Schema Is Modeled With A Hierarchical Hidden Markov Model, And Meanwhile It Is Shifted To The Interaction Schema To Generate The Interaction Counts Through Poisson Factorization. A Hybrid Gibbs-Variational Algorithm Is Then Proposed For Efficient Parameter Estimation Of The COSIN Model. We Conduct Extensive Experiments On Both Synthetic And Real-world Telecom Datasets In Different Scales, And The Results Show That The Proposed Model Outperforms Some Competitive Baseline Methods And Is Scalable. A Case Is Further Presented To Show The Precious Explainability Of The Model.

NOVAL BIOMETRIC FOR MEDICAL IMAGES A Novel Biometric Inspired Robust Security Framework For Medical Images

The Protection Of Sensitive And Confidential Data Become A Challenging Task In The Present Scenario As More And More Digital Data Is Stored And Transmitted Between The End Users. The Privacy Is Vitally Necessary In Case Of Medical Data, Which Contains The Important Information Of The Patients. In This Article, A Novel Biometric Inspired Medical Encryption Technique Is Proposed Based On Newly Introduced Parameterized All Phase Orthogonal Transformation (PR-APBST), Singular Value, And QR Decomposition. The Proposed Technique Utilizes The Biometrics Of The Patient/owner To Generate A Key Management System To Obtain The Parameters Involved In The Proposed Technique. The Medical Image Is Then Encrypted Employing PR-APBST, QR And Singular Value Decomposition And Is Ready For Secure Transmission Or Storage. Finally, A Reliable Decryption Process Is Employed To Reconstruct The Original Medical Image From The Encrypted Image. The Validity And Feasibility Of The Proposed Framework Have Been Demonstrated Using An Extensive Experiments On Various Medical Images And Security Analysis.

SOCIAL CONTEXTUAL IMAGE RECOMMENDATION A Hierarchical Attention Model For Social Contextual Image Recommendation

Image Based Social Networks Are Among The Most Popular Social Networking Services In Recent Years. With A Tremendous Amount Of Images Uploaded Everyday, Understanding Users' Preferences On User-generated Images And Making Recommendations Have Become An Urgent Need. In Fact, Many Hybrid Models Have Been Proposed To Fuse Various Kinds Of Side Information (e.g., Image Visual Representation, Social Network) And User-item Historical Behavior For Enhancing Recommendation Performance. However, Due To The Unique Characteristics Of The User Generated Images In Social Image Platforms, The Previous Studies Failed To Capture The Complex Aspects That Influence Users' Preferences In A Unified Framework. Moreover, Most Of These Hybrid Models Relied On Predefined Weights In Combining Different Kinds Of Information, Which Usually Resulted In Sub-optimal Recommendation Performance. To This End, In This Paper, We Develop A Hierarchical Attention Model For Social Contextual Image Recommendation. In Addition To Basic Latent User Interest Modeling In The Popular Matrix Factorization Based Recommendation, We Identify Three Key Aspects (i.e., Upload History, Social Influence, And Owner Admiration) That Affect Each User's Latent Preferences, Where Each Aspect Summarizes A Contextual Factor From The Complex Relationships Between Users And Images. After That, We Design A Hierarchical Attention Network That Naturally Mirrors The Hierarchical Relationship (elements In Each Aspects Level, And The Aspect Level) Of Users' Latent Interests With The Identified Key Aspects. Specifically, By Taking Embeddings From State-of-the-art Deep Learning Models That Are Tailored For Each Kind Of Data, The Hierarchical Attention Network Could Learn To Attend Differently To More Or Less Content. Finally, Extensive Experimental Results On Real-world Datasets Clearly Show The Superiority Of Our Proposed Model

DATA ANALYSIS IN COVID-19 Data Analysis In Covid-19 In Various Categories

The COVID-19 Epidemic Has Caused A Large Number Of Human Losses And Havoc In The Economic, Social, Societal, And Health Systems Around The World. Controlling Such Epidemic Requires Understanding Its Characteristics And Behavior, Which Can Be Identified By Collecting And Analyzing The Related Big Data. Big Data Analytics Tools Play A Vital Role In Building Knowledge Required In Making Decisions And Precautionary Measures. However, Due To The Vast Amount Of Data Available On COVID-19 From Various Sources, There Is A Need To Review The Roles Of Big Data Analysis In Controlling The Spread Of COVID-19, Presenting The Main Challenges And Directions Of COVID-19 Data Analysis, As Well As Providing A Framework On The Related Existing Applications And Studies To Facilitate Future Research On COVID-19 Analysis. Therefore, In This Paper, We Conduct A Literature Review To Highlight The Contributions Of Several Studies In The Domain Of COVID-19-based Big Data Analysis. The Study Presents As A Taxonomy Several Applications Used To Manage And Control The Pandemic. Moreover, This Study Discusses Several Challenges Encountered When Analyzing COVID-19 Data. The Findings Of This Paper Suggest Valuable Future Directions To Be Considered For Further Research And Applications.

FAKE REVIEW REMOVAL- Fake Product Review Monitoring And Removal For Genuine Online Product Reviews Using Opinion Mining

As Most Of The People Require Review About A Product Before Spending Their Money On The Product. So People Come Across Various Reviews In The Website But These Reviews Are Genuine Or Fake Is Not Identified By The User. In Some Review Websites Some Good Reviews Are Added By The Product Company People Itself In Order To Make In Order To Produce False Positive Product Reviews. They Give Good Reviews For Many Different Products Manufactured By Their Own Firm. User Will Not Be Able To Find Out Whether The Review Is Genuine Or Fake. To Find Out Fake Review In The Website This “Fake Product Review Monitoring And Removal For Genuine Online Product Reviews Using Opinion Mining” System Is Introduced. This System Will Find Out Fake Reviews Made By Posting Fake Comments About A Product By Identifying The IP Address Along With Review Posting Patterns. User Will Login To The System Using His User Id And Password And Will View Various Products And Will Give Review About The Product. To Find Out The Review Is Fake Or Genuine, System Will Find Out The IP Address Of The User If The System Observe Fake Review Send By The Same IP Address Many A Times It Will Inform The Admin To Remove That Review From The System. This System Uses Data Mining Methodology. This System Helps The User To Find Out Correct Review Of The Product.

THYROID DISEASE USING DATA MINING- Prediction For Thyroid Disease Using Data Mining Technique

Classification Based Data Mining Plays Important Role In Various Healthcare Services. In Healthcare Field, The Important And Challenging Task Is To Diagnose Health Conditions And Proper Treatment Of Disease At The Early Stage. There Are Various Diseases That Can Be Diagnosed Early And Can Be Treated At The Early Stage. As For Example, Thyroid Diseases. The Traditional Ways Of Diagnosing Thyroid Diseases Depends On Clinical Examination And Many Blood Tests. The Main Task Is To Detect Disease Diagnosis At The Early Stages With Higher Accuracy. Data Mining Techniques Plays An Important Role In Healthcare Field For Making Decision, Disease Diagnosis And Providing Better Treatment For The Patients At Low Cost. Thyroid Disease Classification Is An Important Task. The Purpose Of This Study Is Predication Of Thyroid Disease Using Different Classification Techniques And Also To Find The TSH, T3,T4 Correlation Towards Hyperthyroidism And Hyporthyroidism And Also To Finding The TSH, T3,T4 Correlation With Gender Towards Hyperthyroidism And Hyporthyroidism.

HEALTH EXAMINATION RECORDS- Mining Health Examination Records - A Graph Based Approach

General Health Examination Is An Integral Part Of Healthcare In Many Countries. Identifying The Participants At Risk Is Important For Early Warning And Preventive Intervention. The Fundamental Challenge Of Learning A Classification Model For Risk Prediction Lies In The Unlabeled Data That Constitutes The Majority Of The Collected Dataset. Particularly, The Unlabeled Data Describes The Participants In Health Examinations Whose Health Conditions Can Vary Greatly From Healthy To Very-ill. There Is No Ground Truth For Differentiating Their States Of Health. In This Paper, We Propose A Graph-based, Semi-supervised Learning Algorithm Called SHG-Health (Semi-supervised Heterogeneous Graph On Health) For Risk Predictions To Classify A Progressively Developing Situation With The Majority Of The Data Unlabeled. An Efficient Iterative Algorithm Is Designed And The Proof Of Convergence Is Given. Extensive Experiments Based On Both Real Health Examination Datasets And Synthetic Datasets Are Performed To Show The Effectiveness And Efficiency Of Our Method.

RANWAR RANKING1-Ranwar Ranking Based Weighted Association Rule Mining From Gene Expression And Methylation Data

Ranking Of Association Rules Is Currently An Interesting Topic In Data Mining And Bioinformatics. The Huge Number Of Evolved Rules Of Items (or, Genes) By Association Rule Mining (ARM) Algorithms Makes Confusion To The Decision Maker. In This Article, We Propose A Weighted Rule-mining Technique (say, $RANWAR$ Or Rank-based Weighted Association Rule-mining) To Rank The Rules Using Two Novel Rule-interestingness Measures, Viz., Rank-based Weighted Condensed Support $(wcs)$ And Weighted Condensed Confidence $(wcc)$ Measures To Bypass The Problem. These Measures Are Basically Depended On The Rank Of Items (genes). Using The Rank, We Assign Weight To Each Item. $RANWAR$ Generates Much Less Number Of Frequent Itemsets Than The State-of-the-art Association Rule Mining Algorithms. Thus, It Saves Time Of Execution Of The Algorithm. We Run $RANWAR$ On Gene Expression And Methylation Datasets. The Genes Of The Top Rules Are Biologically Validated By Gene Ontologies (GOs) And KEGG Pathway Analyses. Many Top Ranked Rules Extracted From $RANWAR$ That Hold Poor Ranks In Traditional Apriori, Are Highly Biologically Significant To The Related Diseases. Finally, The Top Rules Evolved From $RANWAR$ , That Are Not In Apriori, Are Reported.

OUTLIER DETEECTION- An Efficient Approach For Outlier Detection With Imperfect Data Labels

The Task Of Outlier Detection Is To Identify Data Objects That Are Markedly Different From Or Inconsistent With The Normal Set Of Data. Most Existing Solutions Typically Build A Model Using The Normal Data And Identify Outliers That Do Not Fit The Represented Model Very Well. However, In Addition To Normal Data, There Also Exist Limited Negative Examples Or Outliers In Many Applications, And Data May Be Corrupted Such That The Outlier Detection Data Is Imperfectly Labeled. These Make Outlier Detection Far More Difficult Than The Traditional Ones. This Paper Presents A Novel Outlier Detection Approach To Address Data With Imperfect Labels And Incorporate Limited Abnormal Examples Into Learning. To Deal With Data With Imperfect Labels, We Introduce Likelihood Values For Each Input Data Which Denote The Degree Of Membership Of An Example Toward The Normal And Abnormal Classes Respectively. Our Proposed Approach Works In Two Steps. In The First Step, We Generate A Pseudo Training Dataset By Computing Likelihood Values Of Each Example Based On Its Local Behavior. We Present Kernel \(k\) -means Clustering Method And Kernel LOF-based Method To Compute The Likelihood Values. In The Second Step, We Incorporate The Generated Likelihood Values And Limited Abnormal Examples Into SVDD-based Learning Framework To Build A More Accurate Classifier For Global Outlier Detection. By Integrating Local And Global Outlier Detection, Our Proposed Method Explicitly Handles Data With Imperfect Labels And Enhances The Performance Of Outlier Detection. Extensive Experiments On Real Life Datasets Have Demonstrated That Our Proposed Approaches Can Achieve A Better Tradeoff Between Detection Rate And False Alarm Rate As Compared To State-of-the-art Outlier Detection Approaches.

PERDICTION DIFFICULT KEYWORD- Efficient Prediction Of Difficult Keyword Queries Over Database

Keyword Queries On Databases Provide Easy Access To Data, But Often Suffer From Low Ranking Quality, I.e., Low Precision And/or Recall, As Shown In Recent Benchmarks. It Would Be Useful To Identify Queries That Are Likely To Have Low Ranking Quality To Improve The User Satisfaction. For Instance, The System May Suggest To The User Alternative Queries For Such Hard Queries. In This Paper, We Analyze The Characteristics Of Hard Queries And Propose A Novel Framework To Measure The Degree Of Difficulty For A Keyword Query Over A Database, Considering Both The Structure And The Content Of The Database And The Query Results. We Evaluate Our Query Difficulty Prediction Model Against Two Effectiveness Benchmarks For Popular Keyword Search Ranking Methods. Our Empirical Results Show That Our Model Predicts The Hard Queries With High Accuracy. Further, We Present A Suite Of Optimizations To Minimize The Incurred Time Overhead.

MEDICAL DISEASE TREATMENT SYSTEM- Automatic Medical Disease Treatment System For Data Mining

In Our Proposed System Is Identifying Reliable Information In The Medical Domain Stand As Building Blocks For A Healthcare System That Is Up-to-date With The Latest Discoveries. By Using The Tools Such As NLP, ML Techniques. In This Research, Focus On Diseases And Treatment Information, And The Relation That Exists Between These Two Entities. The Main Goal Of This Research Is To Identify The Disease Name With The Symptoms Specified And Extract The Sentence From The Article And Get The Relation That Exists Between Disease-Treatment And Classify The Information Into Cure, Prevent, Side Effect To The User.This Electronic Document Is A “live” Template. The Various Components Of Your Paper [title, Text, Heads, Etc.] Are Already Defined On The Style Sheet, As Illustrated By The Portions Given In This Document.

FAST CLUSTERING - A Fast Clustering - Based Feature Subset Selection Algorithm For High Dimensional Data

Feature Selection Involves Identifying A Subset Of The Most Useful Features That Produces Compatible Results As The Original Entire Set Of Features. A Feature Selection Algorithm May Be Evaluated From Both The Efficiency And Effectiveness Points Of View. While The Efficiency Concerns The Time Required To Find A Subset Of Features, The Effectiveness Is Related To The Quality Of The Subset Of Features. Based On These Criteria, A Fast Clustering-based Feature Selection Algorithm (FAST) Is Proposed And Experimentally Evaluated In This Paper. The FAST Algorithm Works In Two Steps. In The First Step, Features Are Divided Into Clusters By Using Graph-theoretic Clustering Methods. In The Second Step, The Most Representative Feature That Is Strongly Related To Target Classes Is Selected From Each Cluster To Form A Subset Of Features. Features In Different Clusters Are Relatively Independent, The Clustering-based Strategy Of FAST Has A High Probability Of Producing A Subset Of Useful And Independent Features. To Ensure The Efficiency Of FAST, We Adopt The Efficient Minimum-spanning Tree (MST) Clustering Method. The Efficiency And Effectiveness Of The FAST Algorithm Are Evaluated Through An Empirical Study. Extensive Experiments Are Carried Out To Compare FAST And Several Representative Feature Selection Algorithms, Namely, FCBF, ReliefF, CFS, Consist, And FOCUS-SF, With Respect To Four Types Of Well-known Classifiers, Namely, The Probability-based Naive Bayes, The Tree-based C4.5, The Instance-based IB1, And The Rule-based RIPPER Before And After Feature Selection. The Results, On 35 Publicly Available Real-world High-dimensional Image, Microarray, And Text Data, Demonstrate That The FAST Not Only Produces Smaller Subsets Of Features But Also Improves The Performances Of The Four Types Of Classifiers.

PATTER CLASSIFIER UNDER ATTACK- Security Evaluation Of Pattern Classifiers Under Attack

Pattern Classification Systems Are Commonly Used In Adversarial Applications, Like Biometric Authentication, Network Intrusion Detection, And Spam Filtering, In Which Data Can Be Purposely Manipulated By Humans To Undermine Their Operation. As This Adversarial Scenario Is Not Taken Into Account By Classical Design Methods, Pattern Classification Systems May Exhibit Vulnerabilities, Whose Exploitation May Severely Affect Their Performance, And Consequently Limit Their Practical Utility. Extending Pattern Classification Theory And Design Methods To Adversarial Settings Is Thus A Novel And Very Relevant Research Direction, Which Has Not Yet Been Pursued In A Systematic Way. In This Paper, We Address One Of The Main Open Issues: Evaluating At Design Phase The Security Of Pattern Classifiers, Namely, The Performance Degradation Under Potential Attacks They May Incur During Operation. We Propose A Framework For Empirical Evaluation Of Classifier Security That Formalizes And Generalizes The Main Ideas Proposed In The Literature, And Give Examples Of Its Use In Three Real Applications. Reported Results Show That Security Evaluation Can Provide A More Complete Understanding Of The Classifier’s Behavior In Adversarial Environments, And Lead To Better Design Choices.

CREDIT CARD DETECTION- Credit Card Fraud Detection Using Hidden Markov Model

Since Past Few Years There Is Tremendous Advancement In Electronic Commerce Technology, And The Use Of Credit Cards Has Dramatically Increased. As Credit Card Becomes The Most Popular Mode Of Payment For Both Online As Well As Regular Purchase, Cases Of Fraud Associated With It Are Also Rising. In This Paper We Present The Necessary Theory To Detect Fraud In Credit Card Transaction Processing Using A Hidden Markov Model (HMM). An HMM Is Initially Trained With The Normal Behavior Of A Cardholder. If An Incoming Credit Card Transaction Is Not Accepted By The Trained HMM With Sufficiently High Probability, It Is Considered To Be Fraudulent. At The Same Time, We Try To Ensure That Genuine Transactions Are Not Rejected By Using An Enhancement To It(Hybrid Model).In Further Sections We Compare Different Methods For Fraud Detection And Prove That Why HMM Is More Preferred Method Than Other Methods.

PRIVACY PRESERVING DATA PUBLISHING -Slicing A New Approach To Privacy Preserving Data Publishing

Several Anonymization Techniques, Such As Generalization And Bucketization, Have Been Designed For Privacy Preserving Microdata Publishing. Recent Work Has Shown That Generalization Loses Considerable Amount Of Information, Especially For High-dimensional Data. Bucketization, On The Other Hand, Does Not Prevent Membership Disclosure And Does Not Apply For Data That Do Not Have A Clear Separation Between Quasi-identifying Attributes And Sensitive Attributes. In This Paper, We Present A Novel Technique Called Slicing, Which Partitions The Data Both Horizontally And Vertically. We Show That Slicing Preserves Better Data Utility Than Generalization And Can Be Used For Membership Disclosure Protection. Another Important Advantage Of Slicing Is That It Can Handle High-dimensional Data. We Show How Slicing Can Be Used For Attribute Disclosure Protection And Develop An Efficient Algorithm For Computing The Sliced Data That Obey The ℓ-diversity Requirement. Our Workload Experiments Confirm That Slicing Preserves Better Utility Than Generalization And Is More Effective Than Bucketization In Workloads Involving The Sensitive Attribute. Our Experiments Also Demonstrate That Slicing Can Be Used To Prevent Membership Disclosure.

REVIEW RATING PREDICTION - Rating Prediction Based On Social Sentiment From Textual Reviews

In Recent Years, We Have Witnessed A Flourish Of Review Websites. It Presents A Great Opportunity To Share Our Viewpoints For Various Products We Purchase. However, We Face An Information Overloading Problem. How To Mine Valuable Information From Reviews To Understand A User's Preferences And Make An Accurate Recommendation Is Crucial. Traditional Recommender Systems (RS) Consider Some Factors, Such As User's Purchase Records, Product Category, And Geographic Location. In This Work, We Propose A Sentiment-based Rating Prediction Method (RPS) To Improve Prediction Accuracy In Recommender Systems. Firstly, We Propose A Social User Sentimental Measurement Approach And Calculate Each User's Sentiment On Items/products. Secondly, We Not Only Consider A User's Own Sentimental Attributes But Also Take Interpersonal Sentimental Influence Into Consideration. Then, We Consider Product Reputation, Which Can Be Inferred By The Sentimental Distributions Of A User Set That Reflect Customers' Comprehensive Evaluation. At Last, We Fuse Three Factors-user Sentiment Similarity, Interpersonal Sentimental Influence, And Item's Reputation Similarity-into Our Recommender System To Make An Accurate Rating Prediction. We Conduct A Performance Evaluation Of The Three Sentimental Factors On A Real-world Dataset Collected From Yelp. Our Experimental Results Show The Sentiment Can Well Characterize User Preferences, Which Helps To Improve The Recommendation Performance

PRIVACY PRESERVING OUTSOURCED- Privacy-Preserving Outsourced Association Rule Mining On Vertically Partitioned Databases

Association Rule Mining And Frequent Itemset Mining Are Two Popular And Widely Studied Data Analysis Techniques For A Range Of Applications. In This Paper, We Focus On Privacy-preserving Mining On Vertically Partitioned Databases. In Such A Scenario, Data Owners Wish To Learn The Association Rules Or Frequent Itemsets From A Collective Data Set And Disclose As Little Information About Their (sensitive) Raw Data As Possible To Other Data Owners And Third Parties. To Ensure Data Privacy, We Design An Efficient Homomorphic Encryption Scheme And A Secure Comparison Scheme. We Then Propose A Cloud-aided Frequent Itemset Mining Solution, Which Is Used To Build An Association Rule Mining Solution. Our Solutions Are Designed For Outsourced Databases That Allow Multiple Data Owners To Efficiently Share Their Data Securely Without Compromising On Data Privacy. Our Solutions Leak Less Information About The Raw Data Than Most Existing Solutions. In Comparison To The Only Known Solution Achieving A Similar Privacy Level As Our Proposed Solutions, The Performance Of Our Proposed Solutions Is Three To Five Orders Of Magnitude Higher. Based On Our Experiment Findings Using Different Parameters And Data Sets, We Demonstrate That The Run Time In Each Of Our Solutions Is Only One Order Higher Than That In The Best Non-privacy-preserving Data Mining Algorithms. Since Both Data And Computing Work Are Outsourced To The Cloud Servers, The Resource Consumption At The Data Owner End Is Very Low.

HIERARCHAL TENSOR GEOSPATIAL DATA- A Hierarchal Tensor Based Approach To Compressing Updating And Querying Geospatial Data

HIERARCHAL TENSOR GEOSPATIAL DATA- A Hierarchal Tensor Based Approach To Compressing, Updating And Querying Geospatial Data.

MULTI LABEL AND COST SENSITIVE CLASSIFICATION- Generalized K-Label Sets Ensemble For Multi-Label And Cost-Sensitive Classification

Label Powerset (LP) Method Is One Category Of Multi-label Learning Algorithm. This Paper Presents A Basis Expansions Model For Multi-label Classification, Where A Basis Function Is An LP Classifier Trained On A Random K-labelset. The Expansion Coefficients Are Learned To Minimize The Global Error Between The Prediction And The Ground Truth. We Derive An Analytic Solution To Learn The Coefficients Efficiently. We Further Extend This Model To Handle The Cost-sensitive Multi-label Classification Problem, And Apply It In Social Tagging To Handle The Issue Of The Noisy Training Set By Treating The Tag Counts As The Misclassification Costs. We Have Conducted Experiments On Several Benchmark Datasets And Compared Our Method With Other State-of-the-art Multi-label Learning Methods. Experimental Results On Both Multi-label Classification And Cost-sensitive Social Tagging Demonstrate That Our Method Has Better Performance Than Other Methods

FOOD RECOGNITION SYSTEM A Food Recognition System For Diabetic Patients Based On An Optimized Bag Of Features Model

Computer Vision-based Food Recognition Could Be Used To Estimate A Meal's Carbohydrate Content For Diabetic Patients. This Study Proposes A Methodology For Automatic Food Recognition, Based On The Bag-of-features (BoF) Model. An Extensive Technical Investigation Was Conducted For The Identification And Optimization Of The Best Performing Components Involved In The BoF Architecture, As Well As The Estimation Of The Corresponding Parameters. For The Design And Evaluation Of The Prototype System, A Visual Dataset With Nearly 5000 Food Images Was Created And Organized Into 11 Classes. The Optimized System Computes Dense Local Features, Using The Scale-invariant Feature Transform On The HSV Color Space, Builds A Visual Dictionary Of 10000 Visual Words By Using The Hierarchical K-means Clustering And Finally Classifies The Food Images With A Linear Support Vector Machine Classifier. The System Achieved Classification Accuracy Of The Order Of 78%, Thus Proving The Feasibility Of The Proposed Approach In A Very Challenging Image Dataset.

ONLINE IMAGE RETRIEVAL- Mining User Queries With Markov Chains Application To Online Image Retrieval

We Propose A Novel Method For Automatic Annotation, Indexing And Annotation-based Retrieval Of Images. The New Method, That We Call Markovian Semantic Indexing (MSI), Is Presented In The Context Of An Online Image Retrieval System. Assuming Such A System, The Users' Queries Are Used To Construct An Aggregate Markov Chain (AMC) Through Which The Relevance Between The Keywords Seen By The System Is Defined. The Users' Queries Are Also Used To Automatically Annotate The Images. A Stochastic Distance Between Images, Based On Their Annotation And The Keyword Relevance Captured In The AMC, Is Then Introduced. Geometric Interpretations Of The Proposed Distance Are Provided And Its Relation To A Clustering In The Keyword Space Is Investigated. By Means Of A New Measure Of Markovian State Similarity, The Mean First Cross Passage Time (CPT), Optimality Properties Of The Proposed Distance Are Proved. Images Are Modeled As Points In A Vector Space And Their Similarity Is Measured With MSI. The New Method Is Shown To Possess Certain Theoretical Advantages And Also To Achieve Better Precision Versus Recall Results When Compared To Latent Semantic Indexing (LSI) And Probabilistic Latent Semantic Indexing (pLSI) Methods In Annotation-Based Image Retrieval (ABIR) Tasks.

DISTRIBUTED DATABASE- Secure Mining Of Association Rules In Horizontally Distributed Databases

We Propose A Protocol For Secure Mining Of Association Rules In Horizontally Distributed Databases. The Current Leading Protocol Is That Of Kantarcioglu And Clifton . Our Protocol, Like Theirs, Is Based On The Fast Distributed Mining (FDM)algorithm Of Cheung Et Al. , Which Is An Unsecured Distributed Version Of The Apriori Algorithm. The Main Ingredients In Our Protocol Are Two Novel Secure Multi-party Algorithms-one That Computes The Union Of Private Subsets That Each Of The Interacting Players Hold, And Another That Tests The Inclusion Of An Element Held By One Player In A Subset Held By Another. Our Protocol Offers Enhanced Privacy With Respect To The Protocol In . In Addition, It Is Simpler And Is Significantly More Efficient In Terms Of Communication Rounds, Communication Cost And Computational Cost.

DOMAIN KNOWLEDGE- Web-Page Recommendation Based On Web Usage And Domain Knowledge

Web-page Recommendation Plays An Important Role In Intelligent Web Systems. Useful Knowledge Discovery From Web Usage Data And Satisfactory Knowledge Representation For Effective Web-page Recommendations Are Crucial And Challenging. This Paper Proposes A Novel Method To Efficiently Provide Better Web-page Recommendation Through Semantic-enhancement By Integrating The Domain And Web Usage Knowledge Of A Website. Two New Models Are Proposed To Represent The Domain Knowledge. The First Model Uses An Ontology To Represent The Domain Knowledge. The Second Model Uses One Automatically Generated Semantic Network To Represent Domain Terms, Web-pages, And The Relations Between Them. Another New Model, The Conceptual Prediction Model, Is Proposed To Automatically Generate A Semantic Network Of The Semantic Web Usage Knowledge, Which Is The Integration Of Domain Knowledge And Web Usage Knowledge. A Number Of Effective Queries Have Been Developed To Query About These Knowledge Bases. Based On These Queries, A Set Of Recommendation Strategies Have Been Proposed To Generate Web-page Candidates. The Recommendation Results Have Been Compared With The Results Obtained From An Advanced Existing Web Usage Mining (WUM) Method. The Experimental Results Demonstrate That The Proposed Method Produces Significantly Higher Performance Than The WUM Method.

PATTERN IDENTIFICATION - Visualization And Pattern Identification In Large Scale Time Series Data

Visualization Of Massively Large Datasets Presents Two Significant Problems. First, The Dataset Must Be Prepared For Visualization, And Traditional Dataset Manipulation Methods Fail Due To Lack Of Temporary Storage Or Memory. The Second Problem Is The Presentation Of The Data In The Visual Media, Particularly Real-time Visualization Of Streaming Time Series Data. An Ongoing Research Project Addresses Both These Problems, Using Data From Two National Repositories. This Work Is Presented Here, With The Results Of The Current Effort Summarized And Future Plans, Including 3D Visualization, Outlined.

DATA COLLECTION PROTOCOL- A QoS-Aware Data Collection Protocol For LLNs In Fog-Enabled Internet Of Things

Improving Quality Of Service (QoS) Of Low Power And Lossy Networks (LLNs) In Internet Of Things (IoT) Is A Major Challenge. Cluster-based Routing Technique Is An Effective Approach To Achieve This Goal. This Paper Proposes A QoS-aware Clustering-based Routing (QACR) Mechanism For LLNs In Fog-enabled IoT Which Provides A Clustering, A Cluster Head (CH) Election, And A Routing Path Selection Technique. The Clustering Adopts The Community Detection Algorithm That Partitions The Network Into Clusters With Available Nodes' Connectivity. The CH Election And Relay Node Selection Both Are Weighted By The Rank Of The Nodes Which Take Node's Energy, Received Signal Strength, Link Quality, And Number Of Cluster Members Into Consideration As The Ranking Metrics. The Number Of CHs In A Cluster Is Adaptive And Varied According To A Cluster State To Balance The Energy Consumption Of Nodes. Besides, The Protocol Uses The CH Role Handover Technique During CH Election That Decreases The Control Messages For The Periodic Election And Cluster Formation In Detail. An Evaluation Of The QACR Has Performed Through Simulations For Various Scenarios. The Obtained Results Show That The QACR Improves The QoS In Terms Of Packet Delivery Ratio, Latency, And Network Lifetime Compared To The Existing Protocols.

LIGHTWEIGHT GROUP KEY MANAGEMENT- Decentralized Lightweight Group Key Management For Dynamic Access Control In IoT Environments

Rapid Growth Of Internet Of Things (IoT) Devices Dealing With Sensitive Data Has Led To The Emergence Of New Access Control Technologies In Order To Maintain This Data Safe From Unauthorized Use. In Particular, A Dynamic IoT Environment, Characterized By A High Signaling Overhead Caused By Subscribers' Mobility, Presents A Significant Concern To Ensure Secure Data Distribution To Legitimate Subscribers. Hence, For Such Dynamic Environments, Group Key Management (GKM) Represents The Fundamental Mechanism For Managing The Dissemination Of Keys For Access Control And Secure Data Distribution. However, Existing Access Control Schemes Based On GKM And Dedicated To IoT Are Mainly Based On Centralized Models, Which Fail To Address The Scalability Challenge Introduced By The Massive Scale Of IoT Devices And The Increased Number Of Subscribers. Besides, None Of The Existing GKM Schemes Supports The Independence Of The Members In The Same Group. They Focus Only On Dependent Symmetric Group Keys Per Subgroup Communication, Which Is Inefficient For Subscribers With A Highly Dynamic Behavior. To Deal With These Challenges, We Introduce A Novel Decentralized Lightweight Group Key Management Architecture For Access Control In The IoT Environment (DLGKM-AC). Based On A Hierarchical Architecture, Composed Of One Key Distribution Center (KDC) And Several Sub Key Distribution Centers (SKDCs), The Proposed Scheme Enhances The Management Of Subscribers' Groups And Alleviate The Rekeying Overhead On The KDC. Moreover, A New Master Token Management Protocol For Managing Keys Dissemination Across A Group Of Subscribers Is Introduced. This Protocol Reduces Storage, Computation, And Communication Overheads During Join/leave Events. The Proposed Approach Accommodates A Scalable IoT Architecture, Which Mitigates The Single Point Of Failure By Reducing The Load Caused By Rekeying At The Core Network. DLGKM-AC Guarantees Secure Group Communication By Preventing Collusion Attacks And Ensuring Backward/forward Secrecy. Simulation Results And Analysis Of The Proposed Scheme Show Considerable Resource Gain In Terms Of Storage, Computation, And Communication Overheads.

SOFTWAER DEFINED NETWORKS -QoC-Aware Control Traffic Engineering In Software Defined Networks

In A Distributed Software Defined Networking (SDN) Architecture, The Quality Of Service (QoS) Experienced By A Traffic Flow Through An SDN Switch Is Primarily Dependant On The SDN Controller To Which That Switch Is Mapped. We Propose A New Controller-quality Metric Known As The Quality Of Controller (QoC) Which Is Defined Based On The Controller’s Reliability And Response Time. We Model The Controller Reliability Based On Bayesian Inference While Its Response Time Is Modelled As A Linear Approximation Of The M/M/1 Queue. We Develop A QoC-aware Approach For Solving (i) The Switch-controller Mapping Problem And, (ii) Control Traffic Distribution Among The Mapped Controllers. Each Switch Is Mapped To Multiple Controllers To Enable Resilience With The Switch-controller Mapping And Control Traffic Distribution Based On The QoC Metric Which Is The Combined Cost Of Controller Reliability And Response Time. We First Develop An Optimization Programming Formulation That Maximizes The Minimum QoC Among The Set Of Controllers To Solve The Above Problem. Since The Optimization Problem Is Computationally Prohibitive For Large Networks, We Develop A Heuristic Algorithm — Qoc-Aware Switch-coNTroller Mapping (QuANTuM) — That Solves The Problem Of Switch-controller Mapping And Control Traffic Distribution In Two Stages Such That The Minimum Of The Controller QoC Is Maximized. Through Simulations, We Show That The Heuristic Results Are Within 18% Of The Optimum While Achieving A Fair Control Traffic Distribution With A QoC Min-max Ratio Of Up To 95%.

RUMOR MODEL- Rumor Diffusion Model Based On Representation Learning And Anti-Rumor

The Traditional Rumor Diffusion Model Primarily Studies The Rumor Itself And User Behavior As The Entry Points. The Complexity Of User Behavior, Multidimensionality Of The Communication Space, Imbalance Of The Data Samples, And Symbiosis And Competition Between Rumor And Anti-rumor Are Challenges Associated With The In-depth Study On Rumor Communication. Given These Challenges, This Study Proposes A Group Behavior Model For Rumor And Anti-rumor. First, This Study Considers The Diversity And Complexity Of The Rumor Propagation Feature Space And The Advantages Of Representation Learning In The Feature Extraction Of Data. Further, We Adopt The Corresponding Representation Learning Methods For Their Content And Structure Of The Rumor And Anti-rumor To Reduce The Spatial Feature Dimension Of The Rumor-spreading Data And To Uniformly And Densely Express The Full-featured Information Feature Representation. Second, This Paper Introduces An Evolutionary Game Theory, Which Is Combined With The User-influenced Rumor And Anti-rumor, To Reflect The Conflict And Symbiotic Relationship Between Rumor And Anti-rumor. We Obtain A Network Structural Feature Expression Of The Influence Degree Of Users On Rumor And Anti-rumor When Expressing The Structural Characteristics Of Group Communication Relationships. Finally, Aiming At The Timeliness Of Rumor Topic Evolution, The Whole Model Is Proposed. Time Slice And Discretize The Life Cycle Of Rumor Is Used To Synthesize The Full-featured Information Feature Representation Of Rumor And Anti-rumor. The Experiments Denote That The Model Can Not Only Effectively Analyze User Group Behavior Regarding Rumor But Also Accurately Reflect The Competition And Symbiotic Relation Between Rumor And Anti-rumor Diffusion.

LIGHTWEIGHT THREE FACTOR AUTHENTICATION- Lightweight Three-Factor Authentication And Key Agreement Protocol For Internet-Integrated Wireless Sensor Networks

Wireless Sensor Networks (WSNs) Will Be Integrated Into The Future Internet As One Of The Components Of The Internet Of Things, And Will Become Globally Addressable By Any Entity Connected To The Internet. Despite The Great Potential Of This Integration, It Also Brings New Threats, Such As The Exposure Of Sensor Nodes To Attacks Originating From The Internet. In This Context, Lightweight Authentication And Key Agreement Protocols Must Be In Place To Enable End-to-end Secure Communication. Recently, Amin Et Al. Proposed A Three-factor Mutual Authentication Protocol For WSNs. However, We Identified Several Flaws In Their Protocol. We Found That Their Protocol Suffers From Smart Card Loss Attack Where The User Identity And Password Can Be Guessed Using Offline Brute Force Techniques. Moreover, The Protocol Suffers From Known Session-specific Temporary Information Attack, Which Leads To The Disclosure Of Session Keys In Other Sessions. Furthermore, The Protocol Is Vulnerable To Tracking Attack And Fails To Fulfill User Untraceability. To Address These Deficiencies, We Present A Lightweight And Secure User Authentication Protocol Based On The Rabin Cryptosystem, Which Has The Characteristic Of Computational Asymmetry. We Conduct A Formal Verification Of Our Proposed Protocol Using ProVerif In Order To Demonstrate That Our Scheme Fulfills The Required Security Properties. We Also Present A Comprehensive Heuristic Security Analysis To Show That Our Protocol Is Secure Against All The Possible Attacks And Provides The Desired Security Features. The Results We Obtained Show That Our New Protocol Is A Secure And Lightweight Solution For Authentication And Key Agreement For Internet-integrated WSNs.

GEOGRAPHIC AND OPPORTUNISTIC ROUTING- Geographic And Opportunistic Routing For Underwater Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) Have Been Showed As A Promising Technology To Monitor And Explore The Oceans In Lieu Of Traditional Undersea Wireline Instruments. Nevertheless, The Data Gathering Of UWSNs Is Still Severely Limited Because Of The Acoustic Channel Communication Characteristics. One Way To Improve The Data Collection In UWSNs Is Through The Design Of Routing Protocols Considering The Unique Characteristics Of The Underwater Acoustic Communication And The Highly Dynamic Network Topology. In This Paper, We Propose The GEDAR Routing Protocol For UWSNs. GEDAR Is An Anycast, Geographic And Opportunistic Routing Protocol That Routes Data Packets From Sensor Nodes To Multiple Sonobuoys (sinks) At The Sea's Surface. When The Node Is In A Communication Void Region, GEDAR Switches To The Recovery Mode Procedure Which Is Based On Topology Control Through The Depth Adjustment Of The Void Nodes, Instead Of The Traditional Approaches Using Control Messages To Discover And Maintain Routing Paths Along Void Regions. Simulation Results Show That GEDAR Significantly Improves The Network Performance When Compared With The Baseline Solutions, Even In Hard And Difficult Mobile Scenarios Of Very Sparse And Very Dense Networks And For High Network Traffic Loads.

DATA STREAM - Publicly Verifiable Inner Product Evaluation Over Outsourced Data Streams Under Multiple Keys

Uploading Data Streams To A Resource-rich Cloud Server For Inner Product Evaluation, An Essential Building Block In Many Popular Stream Applications (e.g., Statistical Monitoring), Is Appealing To Many Companies And Individuals. On The Other Hand, Verifying The Result Of The Remote Computation Plays A Crucial Role In Addressing The Issue Of Trust. Since The Outsourced Data Collection Likely Comes From Multiple Data Sources, It Is Desired For The System To Be Able To Pinpoint The Originator Of Errors By Allotting Each Data Source A Unique Secret Key, Which Requires The Inner Product Verification To Be Performed Under Any Two Parties' Different Keys. However, The Present Solutions Either Depend On A Single Key Assumption Or Powerful Yet Practically-inefficient Fully Homomorphic Cryptosystems. In This Paper, We Focus On The More Challenging Multi-key Scenario Where Data Streams Are Uploaded By Multiple Data Sources With Distinct Keys. We First Present A Novel Homomorphic Verifiable Tag Technique To Publicly Verify The Outsourced Inner Product Computation On The Dynamic Data Streams, And Then Extend It To Support The Verification Of Matrix Product Computation. We Prove The Security Of Our Scheme In The Random Oracle Model. Moreover, The Experimental Result Also Shows The Practicability Of Our Design.

MULTITASK NETWORKS- Diffusion LMS Over Multitask Networks

The Diffusion LMS Algorithm Has Been Extensively Studied In Recent Years. This Efficient Strategy Allows To Address Distributed Optimization Problems Over Networks In The Case Where Nodes Have To Collaboratively Estimate A Single Parameter Vector. Nevertheless, There Are Several Problems In Practice That Are Multitask-oriented In The Sense That The Optimum Parameter Vector May Not Be The Same For Every Node. This Brings Up The Issue Of Studying The Performance Of The Diffusion LMS Algorithm When It Is Run, Either Intentionally Or Unintentionally, In A Multitask Environment. In This Paper, We Conduct A Theoretical Analysis On The Stochastic Behavior Of Diffusion LMS In The Case Where The Single-task Hypothesis Is Violated. We Analyze The Competing Factors That Influence The Performance Of Diffusion LMS In The Multitask Environment, And Which Allow The Algorithm To Continue To Deliver Performance Superior To Non-cooperative Strategies In Some Useful Circumstances. We Also Propose An Unsupervised Clustering Strategy That Allows Each Node To Select, Via Adaptive Adjustments Of Combination Weights, The Neighboring Nodes With Which It Can Collaborate To Estimate A Common Parameter Vector. Simulations Are Presented To Illustrate The Theoretical Results, And To Demonstrate The Efficiency Of The Proposed Clustering Strategy.

FILE TRANSFER USING CRYPTOGRAPHY TECHNIQUES

In This Project We Are Conducting The Investigation Studies Over The IT Auditing For Assuming The Security For Cloud Computing. During This Investigation, We Are Implementing Working Of IT Auditing Mechanism Over The Cloud Computing Framework In Order To Assure The Desire Level Of Security

MALWARE DETECTION- Behavioral Malware Detection In Delay Tolerant Networks

The Delay-tolerant-network (DTN) Model Is Becoming A Viable Communication Alternative To The Traditional Infrastructural Model For Modern Mobile Consumer Electronics Equipped With Short-range Communication Technologies Such As Bluetooth, NFC, And Wi-Fi Direct. Proximity Malware Is A Class Of Malware That Exploits The Opportunistic Contacts And Distributed Nature Of DTNs For Propagation. Behavioral Characterization Of Malware Is An Effective Alternative To Pattern Matching In Detecting Malware, Especially When Dealing With Polymorphic Or Obfuscated Malware. In This Paper, We First Propose A General Behavioral Characterization Of Proximity Malware Which Based On Naive Bayesian Model, Which Has Been Successfully Applied In Non-DTN Settings Such As Filtering Email Spams And Detecting Botnets. We Identify Two Unique Challenges For Extending Bayesian Malware Detection To DTNs ("insufficient Evidence Versus Evidence Collection Risk" And "filtering False Evidence Sequentially And Distributedly"), And Propose A Simple Yet Effective Method, Look Ahead, To Address The Challenges. Furthermore, We Propose Two Extensions To Look Ahead, Dogmatic Filtering, And Adaptive Look Ahead, To Address The Challenge Of "malicious Nodes Sharing False Evidence." Real Mobile Network Traces Are Used To Verify The Effectiveness Of The Proposed Methods.

SCALE FREE TOPOLOGIES - Constructing Limited Scale-Free Topologies Over Peer-To-Peer Networks

Overlay Network Topology Together With Peer/data Organization And Search Algorithm Are The Crucial Components Of Unstructured Peer-to-peer (P2P) Networks As They Directly Affect The Efficiency Of Search On Such Networks. Scale-free (powerlaw) Overlay Network Topologies Are Among Structures That Offer High Performance For These Networks. A Key Problem For These Topologies Is The Existence Of Hubs, Nodes With High Connectivity. Yet, The Peers In A Typical Unstructured P2P Network May Not Be Willing Or Able To Cope With Such High Connectivity And Its Associated Load. Therefore, Some Hard Cutoffs Are Often Imposed On The Number Of Edges That Each Peer Can Have, Restricting Feasible Overlays To Limited Or Truncated Scale-free Networks. In This Paper, We Analyze The Growth Of Such Limited Scale-free Networks And Propose Two Different Algorithms For Constructing Perfect Scale-free Overlay Network Topologies At Each Instance Of Such Growth. Our Algorithms Allow The User To Define The Desired Scalefree Exponent (gamma). They Also Induce Low Communication Overhead When Network Grows From One Size To Another. Using Extensive Simulations, We Demonstrate That These Algorithms Indeed Generate Perfect Scale Free Networks (at Each Step Of Network Growth) That Provide Better Search Efficiency In Various Search Algorithms Than The Networks Generated By The Existing Solutions.

ENHANCED INTERIOR GATEWAY ROUTING PROTOCOL - Performance Analysis Of Ospf And Eigrp Routing Protocols For Greener Internetworking

Routing Protocol Is Taking A Vital Role In The Modern Internet Era. A Routing Protocol Determines How The Routers Communicate With Each Other To Forward The Packets By Taking The Optimal Path To Travel From A Source Node To A Destination Node. In This Paper We Have Explored Two Eminent Protocols Namely, Enhanced Interior Gateway Routing Protocol (EIGRP) And Open Shortest Path First (OSPF) Protocols. Evaluation Of These Routing Protocols Is Performed Based On The Quantitative Metrics Such As Convergence Time, Jitter, End-to- End Delay, Throughput And Packet Loss Through The Simulated Network Models. The Evaluation Results Show That EIGRP Routing Protocol Provides A Better Performance Than OSPF Routing Protocol For Real Time Applications. Through Network Simulations We Have Proved That EIGRP Is More CPU Intensive Than OSPF And Hence Uses A Lot Of System Power. Therefore EIGRP Is A Greener Routing Protocol And Provides For Greener Internetworking.

HONEYPOTS FOR DETECTING CYBER ATTACK - Creation And Management Of Website Honeypots For Detecting Targeted Cyber Attacks

Message Authentication Is One Of The Most Effective Ways To Thwart Unauthorized And Corrupted Messages From Being Forwarded In Wireless Sensor Networks (WSNs). For This Reason, Many Message Authentication Schemes Have Been Developed, Based On Either Symmetric-key Cryptosystems Or Public-key Cryptosystems. Most Of Them, However, Have The Limitations Of High Computational And Communication Overhead In Addition To Lack Of Scalability And Resilience To Node Compromise Attacks. To Address These Issues, A Polynomial-based Scheme Was Recently Introduced. However, This Scheme And Its Extensions All Have The Weakness Of A Built-in Threshold Determined By The Degree Of The Polynomial: When The Number Of Messages Transmitted Is Larger Than This Threshold, The Adversary Can Fully Recover The Polynomial. In This Paper, We Propose A Scalable Authentication Scheme Based On Elliptic Curve Cryptography (ECC). While Enabling Intermediate Nodes Authentication, Our Proposed Scheme Allows Any Node To Transmit An Unlimited Number Of Messages Without Suffering The Threshold Problem. In Addition, Our Scheme Can Also Provide Message Source Privacy. Both Theoretical Analysis And Simulation Results Demonstrate That Our Proposed Scheme Is More Efficient Than The Polynomial-based Approach In Terms Of Computational And Communication Overhead Under Comparable Security Levels While Providing Message Source Privacy.

HOP BY HOP MESSAGE AUTHENTICATION - Hop-By-Hop Message Authentication And Source Privacy In Wireless Sensor Networks

Message Authentication Is One Of The Most Effective Ways To Thwart Unauthorized And Corrupted Messages From Being Forwarded In Wireless Sensor Networks (WSNs). For This Reason, Many Message Authentication Schemes Have Been Developed, Based On Either Symmetric-key Cryptosystems Or Public-key Cryptosystems. Most Of Them, However, Have The Limitations Of High Computational And Communication Overhead In Addition To Lack Of Scalability And Resilience To Node Compromise Attacks. To Address These Issues, A Polynomial-based Scheme Was Recently Introduced. However, This Scheme And Its Extensions All Have The Weakness Of A Built-in Threshold Determined By The Degree Of The Polynomial: When The Number Of Messages Transmitted Is Larger Than This Threshold, The Adversary Can Fully Recover The Polynomial. In This Paper, We Propose A Scalable Authentication Scheme Based On Elliptic Curve Cryptography (ECC). While Enabling Intermediate Nodes Authentication, Our Proposed Scheme Allows Any Node To Transmit An Unlimited Number Of Messages Without Suffering The Threshold Problem. In Addition, Our Scheme Can Also Provide Message Source Privacy. Both Theoretical Analysis And Simulation Results Demonstrate That Our Proposed Scheme Is More Efficient Than The Polynomial-based Approach In Terms Of Computational And Communication Overhead Under Comparable Security Levels While Providing Message Source Privacy.

SECURE DATA AGGREGATION - Secure Data Aggregation In Wireless Sensor Networks Filtering Out The Attackers Impact

Wireless Sensor Networks (WSNs) Are Increasingly Used In Many Applications, Such As Volcano And Fire Monitoring, Urban Sensing, And Perimeter Surveillance. In A Large WSN, In-network Data Aggregation (i.e., Combining Partial Results At Intermediate Nodes During Message Routing) Significantly Reduces The Amount Of Communication Overhead And Energy Consumption. The Research Community Proposed A Loss-resilient Aggregation Framework Called Synopsis Diffusion, Which Uses Duplicate-insensitive Algorithms On Top Of Multipath Routing Schemes To Accurately Compute Aggregates (e.g., Predicate Count Or Sum). However, This Aggregation Framework Does Not Address The Problem Of False Subaggregate Values Contributed By Compromised Nodes. This Attack May Cause Large Errors In The Aggregate Computed At The Base Station, Which Is The Root Node In The Aggregation Hierarchy. In This Paper, We Make The Synopsis Diffusion Approach Secure Against The Above Attack Launched By Compromised Nodes. In Particular, We Present An Algorithm To Enable The Base Station To Securely Compute Predicate Count Or Sum Even In The Presence Of Such An Attack. Our Attack-resilient Computation Algorithm Computes The True Aggregate By Filtering Out The Contributions Of Compromised Nodes In The Aggregation Hierarchy. Extensive Analysis And Simulation Study Show That Our Algorithm Outperforms Other Existing Approaches.

SECURE DATA RETRIEVAL - Secure Data Retrieval For Decentralized Disruption-Tolerant Military Networks

Mobile Nodes In Military Environments Such As A Battlefield Or A Hostile Region Are Likely To Suffer From Intermittent Network Connectivity And Frequent Partitions. Disruption-tolerant Network (DTN) Technologies Are Becoming Successful Solutions That Allow Wireless Devices Carried By Soldiers To Communicate With Each Other And Access The Confidential Information Or Command Reliably By Exploiting External Storage Nodes. Some Of The Most Challenging Issues In This Scenario Are The Enforcement Of Authorization Policies And The Policies Update For Secure Data Retrieval. Ciphertext-policy Attribute-based Encryption (CP-ABE) Is A Promising Cryptographic Solution To The Access Control Issues. However, The Problem Of Applying CP-ABE In Decentralized DTNs Introduces Several Security And Privacy Challenges With Regard To The Attribute Revocation, Key Escrow, And Coordination Of Attributes Issued From Different Authorities. In This Paper, We Propose A Secure Data Retrieval Scheme Using CP-ABE For Decentralized DTNs Where Multiple Key Authorities Manage Their Attributes Independently. We Demonstrate How To Apply The Proposed Mechanism To Securely And Efficiently Manage The Confidential Data Distributed In The Disruption-tolerant Military Network.

COLLABORATIVE PROTECTION- Firecol - A Collaborative Protection Network For The Detection Of Flooding Ddos Attacks

Distributed Denial-of-service (DDoS) Attacks Are A Major Threat To Security Issues. The Control And Resolving Of DDoS Attacks Is Difficult In A Distributed Network. The Primary Problem Till Date Is The Attacks Are Detected Close To The Victim And Hence Cannot Be Resolved. It Is Essential To Detect Them Early In Order To Protect Vulnerable Resources Or Potential Victims. FireCol Comprises Multiple Intrusion Prevention Systems (IPSs) Located At The Internet Service Providers (ISPs) Level. These Multiple Intrusion Prevention Systems (IPSs) Act As Traffic Filters. Based On Threshold Values It Passes Information. The Efficient System Of FireCol Is Demonstrated As A Scalable System With Low Overhead.

PROFILE MATCHING- Fully Anonymous Profile Matching In Mobile Social Networks

In This Paper, We Study User Profile Matching With Privacy-preservation In Mobile Social Networks (MSNs) And Introduce A Family Of Novel Profile Matching Protocols. We First Propose An Explicit Comparison-based Profile Matching Protocol (eCPM) Which Runs Between Two Parties, An Initiator And A Responder. The ECPM Enables The Initiator To Obtain The Comparison-based Matching Result About A Specified Attribute In Their Profiles, While Preventing Their Attribute Values From Disclosure. We Then Propose An Implicit Comparison-based Profile Matching Protocol (iCPM) Which Allows The Initiator To Directly Obtain Some Messages Instead Of The Comparison Result From The Responder. The Messages Unrelated To User Profile Can Be Divided Into Multiple Categories By The Responder. The Initiator Implicitly Chooses The Interested Category Which Is Unknown To The Responder. Two Messages In Each Category Are Prepared By The Responder, And Only One Message Can Be Obtained By The Initiator According To The Comparison Result On A Single Attribute. We Further Generalize The ICPM To An Implicit Predicate-based Profile Matching Protocol (iPPM) Which Allows Complex Comparison Criteria Spanning Multiple Attributes. The Anonymity Analysis Shows All These Protocols Achieve The Confidentiality Of User Profiles. In Addition, The ECPM Reveals The Comparison Result To The Initiator And Provides Only Conditional Anonymity; The ICPM And The IPPM Do Not Reveal The Result At All And Provide Full Anonymity. We Analyze The Communication Overhead And The Anonymity Strength Of The Protocols. We Then Present An Enhanced Version Of The ECPM, Called ECPM+, By Combining The ECPM With A Novel Prediction-based Adaptive Pseudonym Change Strategy. The Performance Of The ECPM And The ECPM+ Are Comparatively Studied Through Extensive Trace-based Simulations. Simulation Results Demonstrate That The ECPM+ Achieves Significantly Higher Anonymity Strength With Slightly Larger Number Of Pseudonyms Than The ECPM.

DISTRIBUTED SYSTEM- Optimal Client-Server Assignment For Internet Distributed Systems

We Investigate An Underlying Mathematical Model And Algorithms For Optimizing The Performance Of A Class Of Distributed Systems Over The Internet. Such A System Consists Of A Large Number Of Clients Who Communicate With Each Other Indirectly Via A Number Of Intermediate Servers. Optimizing The Overall Performance Of Such A System Then Can Be Formulated As A Client-server Assignment Problem Whose Aim Is To Assign The Clients To The Servers In Such A Way To Satisfy Some Prespecified Requirements On The Communication Cost And Load Balancing. We Show That 1) The Total Communication Load And Load Balancing Are Two Opposing Metrics, And Consequently, Their Tradeoff Is Inherent In This Class Of Distributed Systems; 2) In General, Finding The Optimal Client-server Assignment For Some Prespecified Requirements On The Total Load And Load Balancing Is NP-hard, And Therefore; 3) We Propose A Heuristic Via Relaxed Convex Optimization For Finding The Approximate Solution. Our Simulation Results Indicate That The Proposed Algorithm Produces Superior Performance Than Other Heuristics, Including The Popular Normalized Cuts Algorithm.

SPECTRUM AVAILABITIY- Spectrum-Availabitiy Based Routing For Cognitive Sensor Network

With The Occurrence Of Internet Of Things (IoT) Era, The Proliferation Of Sensors Coupled With The Increasing Usage Of Wireless Spectrums Especially The ISM Band Makes It Difficult To Deploy Real-life IoT. Currently, The Cognitive Radio Technology Enables Sensors Transmit Data Packets Over The Licensed Spectrum Bands As Well As The Free ISM Bands. The Dynamic Spectrum Access Technology Enables Secondary Users (SUs) Access Wireless Channel Bands That Are Originally Licensed To Primary Users. Due To The High Dynamic Of Spectrum Availability, It Is Challenging To Design An Efficient Routing Approach For SUs In Cognitive Sensor Networks. We Estimate The Spectrum Availability And Spectrum Quality From The View Of Both The Global Statistical Spectrum Usage And The Local Instant Spectrum Status, And Then Introduce Novel Routing Metrics To Consider The Estimation. In Our Novel Routing Metrics, One Retransmission Is Allowed To Restrict The Number Of Rerouting And Then Increase The Routing Performance. Then, The Related Two Routing Algorithms According To The Proposed Routing Metrics Are Designed. Finally, Our Routing Algorithms In Extensive Simulations Are Implemented To Evaluate The Routing Performance, And We Find That The Proposed Algorithms Achieve A Significant Performance Improvement Compared With The Reference Algorithm.

CLUSTERING VIEW POINT - Clustering With Multi-Viewpoint Based Similarity Measure

Clustering Is A Useful Technique That Organizes A Large Quantity Of Unordered Text Documents Into A Small Number Of Meaningful And Coherent Cluster, Thereby Providing A Basis For Intuitive And Informative Navigation And Browsing Mechanisms. There Are Some Clustering Methods Which Have To Assume Some Cluster Relationship Among The Data Objects That They Are Applied On. Similarity Between A Pair Of Objects Can Be Defined Either Explicitly Or Implicitly. The Major Difference Between A Traditional Dissimilarity/similarity Measure And Ours Is That The Former Uses Only A Only A Single Viewpoint, Which Is The Origin, While The Latter Utilizes Many Different Viewpoints, Which Are Objects Assumed To Not Be In The Same Cluster With The Two Objects Being Measured. Using Multiple Viewpoints, More Informative Assessment Of Similarity Could Be Achieved. Theoretical Analysis And Empirical Study Are Conducted To Support This Claim. Two Criterion Functions For Document Clustering Are Proposed Based On This New Measure. We Compare Them With Several Well-known Clustering Algorithms That Use Other Popular Similarity Measures On Various Document Collections To Verify The Advantages Of Our Proposal

SPATIAL DATA AGGREGATION- Lightweight And Privacy-Friendly Spatial Data Aggregation For Secure Power Supply And Demand Management In Smart Grids

In This Paper, We Propose A Secret Group-key Generation Scheme In Physical Layer, Where An Arbitrary Number Of Multi-antenna LNs (LN) Exist In Mesh Topology With A Multi-antenna Passive Eavesdropper. In The First Phase Of The Scheme, Pilot Signals Are Transmitted From Selected Antennas Of All Nodes And Each Node Estimates Channels Linked To It. In The Second Phase, Each Node Sequentially Broadcasts A Weighted Combination Of The Estimated Channel Information Using Selected Coefficients. The Other LNs Can Obtain The Channel Information Used For Group-key Generation While The Eavesdropper Cannot. Each Node Then Can Generate A Group Key By Quantizing And Encoding The Estimated Channels Into Keys. We Apply Well-known Quantization Schemes, Such As Scalar And Vector Quantizations, And Compare Their Performance. To Further Enhance The Key-generation Performance, We Also Provide How To Determine The Antennas At Each Node Used For Group-key Generation And The Coefficients Used In The Broadcast Phase. The Simulation Results Verify The Performance Of The Proposed Secret Group-key Generation Scheme Using Various Key-related Metrics. We Also Verify The Practical Robustness Of Our Scheme By Implementing A Testbed Using Universal Software Radio Peripheral. After Generating Secret Common Key Among Three Nodes, We Also Test It Using The National Institute Of Standards And Technology Test Suit. The Generated Key Passes The Test And It Is Random Enough For Communication Secrecy.

DDOS ATTACK DETECTION- Modeling Behavioural Dynamics For Asymmetric Application Layer DDoS Detection

Asymmetric Application Layer DDoS Attacks Using Computationally Intensive HTTP Requests Are An Extremely Dangerous Class Of Attacks Capable Of Taking Down Web Servers With Relatively Few Attacking Connections. These Attacks Consume Limited Network Bandwidth And Are Similar To Legitimate Traffic, Which Makes Their Detection Difficult. Existing Detection Mechanisms For These Attacks Use Indirect Representations Of Actual User Behaviour And Complex Modelling Techniques, Which Leads To A Higher False Positive Rate (FPR) And Longer Detection Time, Which Makes Them Unsuitable For Real Time Use. There Is A Need For Simple, Efficient And Adaptable Detection Mechanisms For Asymmetric DDoS Attacks. In This Work, An Attempt Is Made To Model The Actual Behavioural Dynamics Of Legitimate Users Using A Simple Annotated Probabilistic Timed Automata (PTA) Along With A Suspicion Scoring Mechanism For Differentiating Between Legitimate And Malicious Users. This Allows The Detection Mechanism To Be Extremely Fast And Have A Low FPR. In Addition, The Model Can Incrementally Learn From Run-time Traces, Which Makes It Adaptable And Reduces The FPR Further. Experiments On Public Datasets Reveal That Our Proposed Approach Has A High Detection Rate And Low FPR And Adds Negligible Overhead To The Web Server, Which Makes It Ideal For Real Time Use.

BIOCHEMICAL ATTACK ON DNA FINGERPRINTING Molecular Barcoding As A Defense Against Bench Top Biochemical Attacks On DNA Fingerprinting And Information Forensics

DNA Fingerprinting Can Offer Remarkable Benefits, Especially For Point-of-care Diagnostics, Information Forensics, And Analysis. However, The Pressure To Drive Down Costs Is Likely To Lead To Cheap Untrusted Solutions And A Multitude Of Unprecedented Risks. These Risks Will Especially Emerge At The Frontier Between The Cyberspace And DNA Biology. To Address These Risks, We Perform A Forensic-security Assessment Of A Typical DNA-fingerprinting Flow. We Demonstrate, For The First Time, Benchtop Analysis Of Biochemical-level Vulnerabilities In Flows That Are Based On A Standard Quantification Assay Known As Polymerase Chain Reaction (PCR). After Identifying Potential Vulnerabilities, We Realize Attacks Using Benchtop Techniques To Demonstrate Their Catastrophic Impact On The Outcome Of The DNA Fingerprinting. We Also Propose A Countermeasure, In Which DNA Samples Are Each Uniquely Barcoded (using Synthesized DNA Molecules) In Advance Of PCR Analysis, Thus Demonstrating The Feasibility Of Our Approach Using Benchtop Techniques. We Discuss How Molecular Barcoding Could Be Utilized Within A Cyber-biological Framework To Improve DNA-fingerprinting Security Against A Wide Range Of Threats, Including Sample Forgery. We Also Present A Security Analysis Of The DNA Barcoding Mechanism From A Molecular Biology Perspective.

MULTI ANTENNA MESH TOPOLOGY- Secret Group-Key Generation At Physical Layer For Multi-Antenna Mesh Topology

In This Paper, We Propose A Secret Group-key Generation Scheme In Physical Layer, Where An Arbitrary Number Of Multi-antenna LNs (LN) Exist In Mesh Topology With A Multi-antenna Passive Eavesdropper. In The First Phase Of The Scheme, Pilot Signals Are Transmitted From Selected Antennas Of All Nodes And Each Node Estimates Channels Linked To It. In The Second Phase, Each Node Sequentially Broadcasts A Weighted Combination Of The Estimated Channel Information Using Selected Coefficients. The Other LNs Can Obtain The Channel Information Used For Group-key Generation While The Eavesdropper Cannot. Each Node Then Can Generate A Group Key By Quantizing And Encoding The Estimated Channels Into Keys. We Apply Well-known Quantization Schemes, Such As Scalar And Vector Quantizations, And Compare Their Performance. To Further Enhance The Key-generation Performance, We Also Provide How To Determine The Antennas At Each Node Used For Group-key Generation And The Coefficients Used In The Broadcast Phase. The Simulation Results Verify The Performance Of The Proposed Secret Group-key Generation Scheme Using Various Key-related Metrics. We Also Verify The Practical Robustness Of Our Scheme By Implementing A Testbed Using Universal Software Radio Peripheral. After Generating Secret Common Key Among Three Nodes, We Also Test It Using The National Institute Of Standards And Technology Test Suit. The Generated Key Passes The Test And It Is Random Enough For Communication Secrecy.

PRIVARY PROTECTION BASED ON ANONYMITY- Source-Location Privacy Protection Based On Anonymity Cloud In Wireless Sensor Networks

An Adversary Can Deploy Parasitic Sensor Nodes Into Wireless Sensor Networks To Collect Radio Traffic Distribu-tions And Trace Back Messages To Their Source Nodes. Then, He Can Locate The Monitored Targets Around The Source Nodes With A High Probability. In This Paper, A Source-location Pri-vacy Protection Scheme Based On Anonymity Cloud (SPAC) Is Proposed. We First Design A Light-weight (t,n) -threshold Message Sharing Scheme And Map The Original Message To A Set Of Message Shares Which Are Shorter In Length And Can Be Processed And Delivered With Minimal Energy Consumption. Based On The Shares, The Source Node Constructs An Anonym-ity Cloud With An Irregular Shape Around Itself To Protect Its Location Privacy. Specifically, An Anonymity Cloud Is A Set Of Active Nodes With Similar Radio Actions And They Are Statisti-cally Indistinguishable From Each Other. The Size Of The Cloud Is Controlled By The Preset Number Of Hops That The Shares Can Walk In The Cloud. At The Border Of The Cloud, The Fake Source Nodes Independently Send The Shares To The Sink Node Through Proper Routing Algorithms. At Last, The Original Message Can Be Recovered By The Sink Node Once At Least T Shares Are Re-ceived. Simulation Results Demonstrate That SPAC Can Strongly Protect The Source-location Privacy With An Efficient Manner. Moreover, The Message Sharing Mechanism Of SPAC Increases Confidentiality Of Network Data And It Also Brings High Tolerance For The Failures Of Sensor Nodes To The Data Transmission Process.

ANALYSIS OF ATTACK- Classification Of Attack Types And Analysis Of Attack Methods For Profiling Phishing Mail Attack Groups

In Recent Years, There Has Been An Increase In The Number Of Phishing Attacks Targeting People In The Fields Of Defense, Security, And Diplomacy Around The World. In Particular, Hacking Attack Group Kimsuky Has Been Conducting Phishing Attacks To Collect Key Information From Public Institutions Since 2013. The Main Feature Of The Attack Techniques Used By The Kimsuky Attack Group Are To Conceal Malicious Code In Phishing E-mails Disguised As Normal E-mails To Spread A Document File That Is Vulnerable To Security, Such As A Hangul File, Or To Induce Interest Through A Social Engineering Attack Technique To Collect Account Information. This Study Classified The Types Of Phishing E-mail Attacks Into Spoofed E-mails, E-mail Body Vulnerability Use, And Attached File Spoofing, And Detailed Analyses Of Their Attack Methods, Such As Commonality And Characteristic Analyses, Were Performed To Analyze The Profile Of This Phishing E-mail Attack Group. Based On The Results, The Purpose Of The Attacking Group Was Determined To Be Intelligence Gathering Because It Focused On Phishing Attacks Targeting Korean Diplomatic And Defense Public Institutions And Related Foreign Institutions. Finally, A Countermeasure That Can Be Used By Mail Service Providers And Mail Users To Respond To Phishing E-mails Is Suggested.

KEY DISTRIBUTION OVER OPTICAL BACKBONE NETWORKS- Hybrid Trusted - Untrusted Relay-Based Quantum Key Distribution Over Optical Backbone Networks

Quantum Key Distribution (QKD) Has Demonstrated A Great Potential To Provide Future-proofed Security, Especially For 5G And Beyond Communications. As The Critical Infrastructure For 5G And Beyond Communications, Optical Networks Can Offer A Cost-effective Solution To QKD Deployment Utilizing The Existing Fiber Resources. In Particular, Measurement-device-independent QKD Shows Its Ability To Extend The Secure Distance With The Aid Of An Untrusted Relay. Compared To The Trusted Relay, The Untrusted Relay Has Obviously Better Security, Since It Does Not Rely On Any Assumption On Measurement And Even Allows To Be Accessed By An Eavesdropper. However, It Cannot Extend QKD To An Arbitrary Distance Like The Trusted Relay, Such That It Is Expected To Be Combined With The Trusted Relay For Large-scale QKD Deployment. In This Work, We Study The Hybrid Trusted/untrusted Relay Based QKD Deployment Over Optical Backbone Networks And Focus On Cost Optimization During The Deployment Phase. A New Network Architecture Of Hybrid Trusted/untrusted Relay Based QKD Over Optical Backbone Networks Is Described, Where The Node Structures Of The Trusted Relay And Untrusted Relay Are Elaborated. The Corresponding Network, Cost, And Security Models Are Formulated. To Optimize The Deployment Cost, An Integer Linear Programming Model And A Heuristic Algorithm Are Designed. Numerical Simulations Verify That The Cost-optimized Design Can Significantly Outperform The Benchmark Algorithm In Terms Of Deployment Cost And Security Level. Up To 25% Cost Saving Can Be Achieved By Deploying QKD With The Hybrid Trusted/untrusted Relay Scheme While Keeping Much Higher Security Level Relative To The Conventional Point-to-point QKD Protocols That Are Only With The Trusted Relays.

ONLINE SOCIAL NETWORK DATA SHARING USING PATTER ANALYSIS- Disclose More And Risk Less - Privacy Preserving Online Social Network Data Sharing Using Pattern Analysis

Social Networks Pervaded Human Lives In Mostly Each Aspect. The Vast Amount Of Sensitive Data That Users Produce And Exchanged On These Platforms Call For Intensive Concern About Information And Privacy Protection. Moreover, The Users’ Statistical Usage Data Collected For Analysis Is Also Subject To Leakage And Therefor Require Protection. Although There Is An Availability Of Privacy Preserving Methods, They Are Not Scalable, Or Tend To Underperform When It Comes To Data Utility And Efficiency. Thus, In This Paper, We Develop A Novel Approach For Anonymizing Users’ Statistical Data. The Data Is Collected From The User’s Behavior Patterns In Social Networks. In Particular, We Collect Specific Points From The User’s Behavior Patterns Rather Than The Entire Data Stream To Be Fed Into Local Differential Privacy (LDP). After The Statistical Data Has Been Anonymized, We Reconstruct The Original Points Using Nonlinear Techniques. The Results From This Approach Provide Significant Accuracy When Compared With The Straightforward Anonymization Approach.

SECURE AND PRIVACY PRESERVING- Enforcing Secure And Privacy-Preserving Information Brokering In Distributed Information Sharing

Today's Organizations Raise An Increasing Need For Information Sharing Via On-demand Access. Information Brokering Systems (IBSs) Have Been Proposed To Connect Large-scale Loosely Federated Data Sources Via A Brokering Overlay, In Which The Brokers Make Routing Decisions To Direct Client Queries To The Requested Data Servers. Many Existing IBSs Assume That Brokers Are Trusted And Thus Only Adopt Server-side Access Control For Data Confidentiality. However, Privacy Of Data Location And Data Consumer Can Still Be Inferred From Metadata (such As Query And Access Control Rules) Exchanged Within The IBS, But Little Attention Has Been Put On Its Protection. In This Paper, We Propose A Novel Approach To Preserve Privacy Of Multiple Stakeholders Involved In The Information Brokering Process. We Are Among The First To Formally Define Two Privacy Attacks, Namely Attribute-correlation Attack And Inference Attack, And Propose Two Countermeasure Schemes Automaton Segmentation And Query Segment Encryption To Securely Share The Routing Decision-making Responsibility Among A Selected Set Of Brokering Servers. With Comprehensive Security Analysis And Experimental Results, We Show That Our Approach Seamlessly Integrates Security Enforcement With Query Routing To Provide System-wide Security With Insignificant Overhead.

COOPERATIVE QUERY ANSWER AUTHENTICATION - Cooperative Query Answer Authentication Scheme Over Anonymous Sensing Data

In Cloud Service Over Crowd-sensing Data, The Data Owner (DO) Publishes The Sensing Data Through The Cloud Server, So That The User Can Obtain The Information Of Interest On Demand. But The Cloud Service Providers (CSP) Are Often Untrustworthy. The Privacy And Security Concerns Emerge Over The Authenticity Of The Query Answer And The Leakage Of The DO Identity. To Solve These Issues, Many Researchers Study The Query Answer Authentication Scheme For Cloud Service System. The Traditional Technique Is Providing DO's Signature For The Published Data. But The Signature Would Always Reveal DO's Identity. To Deal With This Disadvantage, This Paper Proposes A Cooperative Query Answer Authentication Scheme, Based On The Ring Signature, The Merkle Hash Tree (MHT) And The Non-repudiable Service Protocol. Through The Cooperation Among The Entities In Cloud Service System, The Proposed Scheme Could Not Only Verify The Query Answer, But Also Protect The DO's Identity. First, It Picks Up The Internal Nodes Of MHT To Sign, As Well As The Root Node. Thus, The Verification Computation Complexity Could Be Significantly Reduced From O(log 2 N) To O(log 2 N 0.5 ) In The Best Case. Then, It Improves An Existing Ring Signature To Sign The Selected Nodes. Furthermore, The Proposed Scheme Employs The Non-repudiation Protocol During The Transmission Of Query Answer And Verification Object To Protect Trading Behavior Between The CSP And Users. The Security And Performance Analysis Prove The Security And Feasibility Of The Proposed Scheme. Extensive Experimental Results Demonstrate Its Superiority Of Verification Efficiency And Communication Overhead

MALWARE DETACTION- Search Rank Fraud And Malware Detection In Google Play

Fraudulent Behaviors In Google Play, The Most Popular Android App Market, Fuel Search Rank Abuse And Malware Proliferation. To Identify Malware, Previous Work Has Focused On App Executable And Permission Analysis. In This Paper, We Introduce FairPlay, A Novel System That Discovers And Leverages Traces Left Behind By Fraudsters, To Detect Both Malware And Apps Subjected To Search Rank Fraud. FairPlay Correlates Review Activities And Uniquely Combines Detected Review Relations With Linguistic And Behavioral Signals Gleaned From Google Play App Data (87 K Apps, 2.9 M Reviews, And 2.4M Reviewers, Collected Over Half A Year), In Order To Identify Suspicious Apps. FairPlay Achieves Over 95 Percent Accuracy In Classifying Gold Standard Datasets Of Malware, Fraudulent And Legitimate Apps. We Show That 75 Percent Of The Identified Malware Apps Engage In Search Rank Fraud. FairPlay Discovers Hundreds Of Fraudulent Apps That Currently Evade Google Bouncer's Detection Technology. FairPlay Also Helped The Discovery Of More Than 1,000 Reviews, Reported For 193 Apps, That Reveal A New Type Of “coercive” Review Campaign: Users Are Harassed Into Writing Positive Reviews, And Install And Review Other Apps.

MALICIOUS APPLICATION-Detecting Malicious Facebook Applications

With 20 Million Installs A Day , Third-party Apps Are A Major Reason For The Popularity And Addictiveness Of Facebook. Unfortunately, Hackers Have Realized The Potential Of Using Apps For Spreading Malware And Spam. The Problem Is Already Significant, As We Find That At Least 13% Of Apps In Our Dataset Are Malicious. So Far, The Research Community Has Focused On Detecting Malicious Posts And Campaigns. In This Paper, We Ask The Question: Given A Facebook Application, Can We Determine If It Is Malicious? Our Key Contribution Is In Developing FRAppE-Facebook's Rigorous Application Evaluator-arguably The First Tool Focused On Detecting Malicious Apps On Facebook. To Develop FRAppE, We Use Information Gathered By Observing The Posting Behavior Of 111K Facebook Apps Seen Across 2.2 Million Users On Facebook. First, We Identify A Set Of Features That Help Us Distinguish Malicious Apps From Benign Ones. For Example, We Find That Malicious Apps Often Share Names With Other Apps, And They Typically Request Fewer Permissions Than Benign Apps. Second, Leveraging These Distinguishing Features, We Show That FRAppE Can Detect Malicious Apps With 99.5% Accuracy, With No False Positives And A High True Positive Rate (95.9%). Finally, We Explore The Ecosystem Of Malicious Facebook Apps And Identify Mechanisms That These Apps Use To Propagate. Interestingly, We Find That Many Apps Collude And Support Each Other; In Our Dataset, We Find 1584 Apps Enabling The Viral Propagation Of 3723 Other Apps Through Their Posts. Long Term, We See FRAppE As A Step Toward Creating An Independent Watchdog For App Assessment And Ranking, So As To Warn Facebook Users Before Installing Apps

PRIVACY PRESERVING LOCATION BASED QUERY- EPLQ Efficient Privacy-Preserving Location -Based Query Over Outsourced Encrypted Data

Now A Day’s Malwares Are Becoming Increasingly Stealthy, More And More Malwares Are Using Cryptographic Algorithms To Protect Themselves From Being Analyzed. To Enable More Effective Malware Analysis, Forensics And Reverse Engineering, We Have Developed CipherXRay – A Novel Binary Analysis Framework That Can Automatically Identify And Recover The Cryptographic Operations And Transient Secrets From The Execution Of Potentially Obfuscated Binary Executables. Based On The Avalanche Effect Of Cryptographic Functions, CipherXRay Is Able To Accurately Pinpoint The Boundary Of Cryptographic Operation And Recover Truly Transient Cryptographic Secrets That Only Exist In Memory For One Instant In Between Multiple Nested Cryptographic Operations. In Existing Mechanism Not Fully Detect The Malwares. Our Proposed Method CipherXRay Can Further Identify Certain Operation Modes Of The Identified Block Cipher And Tell Whether The Identified Block Cipher Operation Is Encryption Or Decryption In Certain Cases.

CIPHERXRAY CRYPTOGRAPHIC OPERATIONS- Exposing Cryptographic Operations And Transient Secrets From Monitored Binary Execution

Malwares Are Becoming Increasingly Stealthy, More And More Malwares Are Using Cryptographic Algorithms (e.g., Packing, Encrypting C&C Communication) To Protect Themselves From Being Analyzed. The Use Of Cryptographic Algorithms And Truly Transient Cryptographic Secrets Inside The Malware Binary Imposes A Key Obstacle To Effective Malware Analysis And Defense. To Enable More Effective Malware Analysis, Forensics, And Reverse Engineering, We Have Developed CipherXRay - A Novel Binary Analysis Framework That Can Automatically Identify And Recover The Cryptographic Operations And Transient Secrets From The Execution Of Potentially Obfuscated Binary Executables. Based On The Avalanche Effect Of Cryptographic Functions, CipherXRay Is Able To Accurately Pinpoint The Boundary Of Cryptographic Operation And Recover Truly Transient Cryptographic Secrets That Only Exist In Memory For One Instant In Between Multiple Nested Cryptographic Operations. CipherXRay Can Further Identify Certain Operation Modes (e.g., ECB, CBC, CFB) Of The Identified Block Cipher And Tell Whether The Identified Block Cipher Operation Is Encryption Or Decryption In Certain Cases. We Have Empirically Validated CipherXRay With OpenSSL, Popular Password Safe KeePassX, The Ciphers Used By Malware Stuxnet, Kraken And Agobot, And A Number Of Third Party Softwares With Built-in Compression And Checksum. CipherXRay Is Able To Identify Various Cryptographic Operations And Recover Cryptographic Secrets That Exist In Memory For Only A Few Microseconds. Our Results Demonstrate That Current Software Implementations Of Cryptographic Algorithms Hardly Achieve Any Secrecy If Their Execution Can Be Monitored.

INTRUSION DETECTION-Network Intrusion Detection And Countermeasure Selection In Virtual Network Systems-NICE

Cloud Security Is One Of Most Important Issues That Has Attracted A Lot Of Research And Development Effort In Past Few Years. Particularly, Attackers Can Explore Vulnerabilities Of A Cloud System And Compromise Virtual Machines To Deploy Further Large-scale Distributed Denial-of-Service (DDoS). DDoS Attacks Usually Involve Early Stage Actions Such As Multi-step Exploitation, Low Frequency Vulnerability Scanning, And Compromising Identified Vulnerable Virtual Machines As Zombies, And Finally DDoS Attacks Through The Compromised Zombies. Within The Cloud System, Especially The Infrastructure-as-a-Service (IaaS) Clouds, The Detection Of Zombie Exploration Attacks Is Extremely Difficult. This Is Because Cloud Users May Install Vulnerable Applications On Their Virtual Machines. To Prevent Vulnerable Virtual Machines From Being Compromised In The Cloud, We Propose A Multi-phase Distributed Vulnerability Detection, Measurement, And Countermeasure Selection Mechanism Called NICE, Which Is Built On Attack Graph Based Analytical Models And Reconfigurable Virtual Network-based Countermeasures. The Proposed Framework Leverages OpenFlow Network Programming APIs To Build A Monitor And Control Plane Over Distributed Programmable Virtual Switches In Order To Significantly Improve Attack Detection And Mitigate Attack Consequences. The System And Security Evaluations Demonstrate The Efficiency And Effectiveness Of The Proposed Solution.

ROUTING PROTOCOL - EIGRP Routing Protocol In Wireless Ad Hoc Sensor Networks

Routing Protocol Is Taking A Vital Role In The Modern Internet Era. A Routing Protocol Determines How The Routers Communicate With Each Other To Forward The Packets By Taking The Optimal Path To Travel From A Source Node To A Destination Node. In This Paper We Have Explored Two Eminent Protocols Namely, Enhanced Interior Gateway Routing Protocol (EIGRP) And Open Shortest Path First (OSPF) Protocols. Evaluation Of These Routing Protocols Is Performed Based On The Quantitative Metrics Such As Convergence Time, Jitter, End-to- End Delay, Throughput And Packet Loss Through The Simulated Network Models. The Evaluation Results Show That EIGRP Routing Protocol Provides A Better Performance Than OSPF Routing Protocol For Real Time Applications. Through Network Simulations We Have Proved That EIGRP Is More CPU Intensive Than OSPF And Hence Uses A Lot Of System Power. Therefore EIGRP Is A Greener Routing Protocol And Provides For Greener Internetworking.

CLICK POINT- Secure Process Monitoring In Cooperative Bank Using Click Point

Every Customer Should Have Confidential Information. These Are Wants To Maintain In A Secure Manner. Online Banking System Can Be Considered As The One Of The Great Tool Supporting Many Customers As Well As Banks And Financial Institutions To Make May Bank Activities. Every Day Banks Need To Perform Many Activities Related To Users Which Needs Huge Infrastructure With More Staff Members Etc. But The Online Banking System Allows The Banks To Perform These Activities In A Simpler Way Without Involving The Employees For Example Consider Online Banking, Mobile Banking And ATM Banking. But Banking System Needs To Be More Secure And Reliable Because Each And Every Task Performed Is Related To Customer’s Money. Especially Authentication And Validation Of User Access Is The Major Task In The Banking Systems. Usable Security Has Unique Usability Challenges Because The Need For Security Often Means That Standard Human-computer-interaction Approaches Cannot Be Directly Applied. An Important Usability Goal For Authentication Systems Is To Support Users In Selecting Better Passwords. Users Often Create Memorable Passwords That Are Easy For Attackers To Guess, But Strong System-assigned Passwords Are Difficult For Users To Remember. So Researchers Of Modern Days Have Gone For Alternative Methods Wherein Graphical Pictures Are Used As Passwords. The Major Goal Of This Work Is To Reduce The Guessing Attacks As Well As Encouraging Users To Select More Random, And Difficult Passwords To Guess. Well Known Security Threats Like Brute Force Attacks And Dictionary Attacks Can Be Successfully Abolished Using This Method.

SYBIL DEFENDER - A Defense Mechanism For Sybil Attacks In Large Social Networks

Distributed Systems Without Trusted Identities Are Particularly Vulnerable To Sybil Attacks, Where An Adversary Creates Multiple Bogus Identities To Compromise The Running Of The System. This Paper Presents SybilDefender, A Sybil Defense Mechanism That Leverages The Network Topologies To Defend Against Sybil Attacks In Social Networks. Based On Performing A Limited Number Of Random Walks Within The Social Graphs, SybilDefender Is Efficient And Scalable To Large Social Networks. Our Experiments On Two 3,000,000 Node Real-world Social Topologies Show That SybilDefender Outperforms The State Of The Art By More Than 10 Times In Both Accuracy And Running Time. SybilDefender Can Effectively Identify The Sybil Nodes And Detect The Sybil Community Around A Sybil Node, Even When The Number Of Sybil Nodes Introduced By Each Attack Edge Is Close To The Theoretically Detectable Lower Bound. Besides, We Propose Two Approaches To Limiting The Number Of Attack Edges In Online Social Networks. The Survey Results Of Our Facebook Application Show That The Assumption Made By Previous Work That All The Relationships In Social Networks Are Trusted Does Not Apply To Online Social Networks, And It Is Feasible To Limit The Number Of Attack Edges In Online Social Networks By Relationship Rating.

PASSWORD AUTHENTICATED GROUP KEY AGREEMENT- Password-Authenticated Group Key Agreement With Adaptive Security And Contributiveness

Adaptively-secure Key Exchange Allows The Establishment Of Secure Channels Even In The Presence Of An Adversary That Can Corrupt Parties Adaptively And Obtain Their Internal States. In This Paper, We Give A Formal Definition Of Contributory Protocols And Define An Ideal Functionality For Password-based Group Key Exchange With Explicit Authentication And Contributiveness In The UC Framework. As With Previous Definitions In The Same Framework, Our Definitions Do Not Assume Any Particular Distribution On Passwords Or Independence Between Passwords Of Different Parties. We Also Provide The First Steps Toward Realizing This Functionality In The Above Strong Adaptive Setting By Analyzing An Efficient Existing Protocol And Showing That It Realizes The Ideal Functionality In The Random-oracle And Ideal-cipher Models Based On The CDH Assumption.

Online Password Guessing Attacks

Passwords Are The Most Commonly Used Means Of Authentication As Passwords Are Very Convenient For Users, Easier To Implement And User Friendly. Password Based Systems Suffer From Two Types Of Attacks: I) Offline Attacks Ii) Online Attacks. Eavesdropping The Communication Channel And Recording The Conversations Taking Place On The Communication Channel Is An Example For Offline Attack. Brute Force And Dictionary Attacks Are The Two Types Of Online Attacks Which Are Widespread And Increasing. Enabling Convenient Login For Legitimate Users While Preventing Such Attacks Is A Difficult Problem. The Proposed Protocol Called Password Guessing Resistant Protocol (PGRP), Helps In Preventing Such Attacks And Provides A Pleasant Login Experience For Legitimate Users. PGRP Limits The Number Of Login Attempts For Unknown Users To One, And Then Challenges The Unknown User With An Automated Turing Test (ATT). There Are Different Kinds Of ATT Tests Such As CAPTCHA (Completely Automated Public Turing Test To Tell Computers And Humans Apart), Security Questions Etc. In This System, A Distorted Textbased CAPTCHA Is Used. If The ATT Test Is Correctly Answered, The User Is Granted Access Else The User Is Denied Access. The Proposed Algorithm Analyzes The Efficiency Of PGRP Based On Three Conditions: I) Number Of Successful Login Attempts Ii) Number Of Failed Login Attempts With Invalid Password Iii) Number Of Failed Login Attempts With Invalid Password And ATT Test. PGRP Log Files Are Used As Data Sets. The Analysis Helps In Determining The Efficiency Of PGRP Protocol.

VEHICULAR COMMUNICATION- An Efficient Message Authentication Scheme For Vehicular Communication

In This Paper, We Introduce A Novel Roadside Unit (RSU)-aided Message Authentication Scheme Named RAISE, Which Makes RSUs Responsible For Verifying The Authenticity Of Messages Sent From Vehicles And For Notifying The Results Back To Vehicles. In Addition, RAISE Adopts The $k$- Anonymity Property For Preserving User Privacy, Where A Message Cannot Be Associated With A Common Vehicle. In The Case Of The Absence Of An RSU, We Further Propose A Supplementary Scheme, Where Vehicles Would Cooperatively Work To Probabilistically Verify Only A Small Percentage Of These Message Signatures Based On Their Own Computing Capacity. Extensive Simulations Are Conducted To Validate The Proposed Scheme. It Is Demonstrated That RAISE Yields A Much Better Performance Than Previously Reported Counterparts In Terms Of Message Loss Ratio (LR) And Delay.

DETECTION AND PREVENTION OF SYBIL ATTACK- Detection And Prevention Of Sybil Attack In MANET Using Mac Address

A Mobile Adhoc Network Is A Network That Does Not Relay On Fixed Infrastructure .It Is A Collection Of Independent Mobile Nodes That Can Communicate To Each Other Via Radio Waves. These Networks Are Fully Distributed, And Can Work At Any Place Without The Help Of Any Fixed Infrastructure As Access Points Or Base Stations. As In Ad- Hoc Network Communication Medium Is Air So It Would Be Easy For Attacker To Fetch Information From Air Medium Using Sniffing Software Tool. There Is An Attack Which Causes So Much Destruction To A Network Called Sybil Attack. In The Sybil Attack A Single Node Presents Multiple Fake Identities To Other Nodes In The Network. In This Research, We Implemented The Sybil Attack Detection Technique Which Is Used To Detect The Sybil Nodes In The Network And Also Prevent It. Simulation Tool Used For The Implementation Is NS2.35.

VOTING SYSTEM - Voting Systems With Trust Mechanisms In Cyberspace Vulnerabilities And Defenses

With The Popularity Of Voting Systems In Cyberspace, There Is Growing Evidence That Current Voting Systems Can Be Manipulated By Fake Votes. This Problem Has Attracted Many Researchers Working On Guarding Voting Systems In Two Areas: Relieving The Effect Of Dishonest Votes By Evaluating The Trust Of Voters, And Limiting The Resources That Can Be Used By Attackers, Such As The Number Of Voters And The Number Of Votes. In This Paper, We Argue That Powering Voting Systems With Trust And Limiting Attack Resources Are Not Enough. We Present A Novel Attack Named As Reputation Trap (RepTrap). Our Case Study And Experiments Show That This New Attack Needs Much Less Resources To Manipulate The Voting Systems And Has A Much Higher Success Rate Compared With Existing Attacks. We Further Identify The Reasons Behind This Attack And Propose Two Defense Schemes Accordingly. In The First Scheme, We Hide Correlation Knowledge From Attackers To Reduce Their Chance To Affect The Honest Voters. In The Second Scheme, We Introduce Robustness-of-evidence, A New Metric, In Trust Calculation To Reduce Their Effect On Honest Voters. We Conduct Extensive Experiments To Validate Our Approach. The Results Show That Our Defense Schemes Not Only Can Reduce The Success Rate Of Attacks But Also Significantly Increase The Amount Of Resources An Adversary Needs To Launch A Successful Attack.

HIDING DATA USING EFFICIENT COMBINATION OF RSA CRYPTOGRAPHY AND COMPRESSION STEGANOGRAPHY TECHNIQUES

Data Compression Is An Important Part Of Information Security Because Compressed Data Is More Secure And Easy To Handle. Effective Data Compression Technology Creates Efficient, Secure, And Easy-to-connect Data. There Are Two Types Of Compression Algorithm Techniques, Lossy And Lossless. These Technologies Can Be Used In Any Data Format Such As Text, Audio, Video, Or Image File. The Main Objective Of This Study Was To Reduce The Physical Space On The Various Storage Media And Reduce The Time Of Sending Data Over The Internet With A Complete Guarantee Of Encrypting This Data And Hiding It From Intruders. Two Techniques Are Implemented, With Data Loss (Lossy) And Without Data Loss (Lossless). In The Proposed Paper A Hybrid Data Compression Algorithm Increases The Input Data To Be Encrypted By RSA (Rivest–Shamir–Adleman) Cryptography Method To Enhance The Security Level And It Can Be Used In Executing Lossy And Lossless Compacting Steganography Methods. This Technique Can Be Used To Decrease The Amount Of Every Transmitted Data Aiding Fast Transmission While Using Slow Internet Or Take A Small Space On Different Storage Media. The Plain Text Is Compressed By The Huffman Coding Algorithm, And Also The Cover Image Is Compressed By Discrete Wavelet Transform DWT Based That Compacts The Cover Image Through Lossy Compression In Order To Reduce The Cover Image’s Dimensions.

ONLINE VOTING SYSTEM THROUGH FINGERPRINT RECONGITION- Biometrics Voting System Through Fingerprint Recognition On A Touch Screen

Fingerprint Biometric Is The Most Widely Deployed Publicized Biometrics For Identification. This Is Largely Due To Its Easy And Cost Effective Integration In Existing And Upcoming Technologies. The Integration Of Biometric With Electronic Voting Machine Undoubtedly Requires Less Manpower, Save Much Time Of Voters And Personal, Eliminate Rigging, Ensure Accuracy, Transparency And Fast Results In Election. In This Paper, A Framework For Electronic Voting Machine Based On Biometric Verification Is Proposed And Implemented. The Proposed Framework Ensures Secured Identification And Authentication Processes For The Voters And Candidates Through The Use Of Fingerprint Biometrics. It Deals With The Design And Development Of An Electronic Voting System Using Fingerprint Recognition. It Allows The Voter To Scan Their Fingerprint, Which Is Then Matched With An Already Saved Image Within A Database. Upon Completion Of Voter Identification, Voters Are Allowed To Cast Their Vote Using LCD And Keypad Interface. Casted Vote Will Be Updated Immediately, Making The System Fast, Efficient And Fraud-free.

ONLINE EXAM PORTAL SERVICES FOR STUDENTS BEHAVIOR- Online Exam Portal Services For Students Behavior And Success Based On Log In Moodle

Today, It Is Almost Impossible To Implement Teaching Processes Without Using Information And Communication Technologies (ICT), Especially In Higher Education.

TEXT FILE HIDING USING STEGANOGRAPHY- Text File Hiding In Audio Files Using Low Bit Encoding Steganography

This Method Is Alternative To Cryptography Techniques Which Provide More Security Than Existing Techniques. Steganography Is The Practice Of Hiding Private Or Sensitive Information Within Something That Appears To Be Nothing Out Of The Usual. Steganography Is Often Confused With Cryptology Because The Two Are Similar In The Way That They Both Are Used To Protect Important Information. The Difference Between The Two Is That Steganography Involves Hiding Information So It Appears That No Information Is Hidden At All. If A Person Or Persons Views The Object That The Information Is Hidden Inside Of He Or She Will Have No Idea That There Is Any Hidden Information, Therefore The Person Will Not Attempt To Decrypt The Information. Steganography In The Modern Day Sense Of The Word Usually Refers To Information Or A File That Has Been Concealed Inside A Digital Picture, Video Or Audio File. What Steganography Essentially Does Is Exploit Human Perception, Human Senses Are Not Trained To Look For Files That Have Information Hidden Inside Of Them, Although There Are Programs Available That Can Do What Is Called Steganalysis (Detecting Use Of Steganography.) The Most Common Use Of Steganography Is To Hide A File Inside Another File. When Information Or A File Is Hidden Inside A Carrier File, The Data Is Usually Encrypted With A Password.

NOISE ROBUST FOR TEMPLATE MATCHING- Asymmetric Correlation A Noise Robust Similarity Measure For Template Matching

We Present An Efficient And Noise Robust Template Matching Method Based On Asymmetric Correlation (ASC). The ASC Similarity Function Is Invariant To Affine Illumination Changes And Robust To Extreme Noise. It Correlates The Given Non-normalized Template With A Normalized Version Of Each Image Window In The Frequency Domain. We Show That This Asymmetric Normalization Is More Robust To Noise Than Other Cross Correlation Variants, Such As The Correlation Coefficient. Direct Computation Of ASC Is Very Slow, As A DFT Needs To Be Calculated For Each Image Window Independently. To Make The Template Matching Efficient, We Develop A Much Faster Algorithm, Which Carries Out A Prediction Step In Linear Time And Then Computes DFTs For Only A Few Promising Candidate Windows. We Extend The Proposed Template Matching Scheme To Deal With Partial Occlusion And Spatially Varying Light Change. Experimental Results Demonstrate The Robustness Of The Proposed ASC Similarity Measure Compared To State-of-the-art Template Matching Methods.

ADAPATIVE STREAMING SERVICES- Self Annihilating Data Storage And Location Based Adaptive Video Streaming Service

Cloud Computing Has Become A Popular Buzzword And It Has Been Widely Used To Refer To Different Technologies, Services, And Concepts. With The Use Of Cloud Computing, Here We Are Trying To Give The Location Based Efficient Video Information To The Mobile Users. Location Based Service(LBS) Is An Information Service And Has A Number Of Uses In Social Networking Today As An Entertainment Service, Which Is Accessible With Mobile Devices Through The Mobile Network And Which Uses Information On The Geographical Position Of The Mobile Device. While The Demands Of Video Streaming Services Over The Mobile Networks Have Been Souring Over These Years, The Wireless Link Capacity Cannot Practically Keep Up With The Growing Traffic Load. In This Project, We Propose And Discuss A Adaptive Video Streaming Framework To Improve The Quality Of Video Services In The Location Based Manner. Through This System, Video Content Can Be Segmented By An Automatic Shot/scene Retrieval Technology And Stored In The Database (DB). In The Client Side, Two Threads Will Be Formed. One Is For Video Streaming And Another One Is For Location Searching And Updating. For The Security Purpose, We Are Using Self Destruction Algorithm Where The Uploaded Video Is Been Destructed Automatically After The User Defined Time. Thus The Location Based Video Information Can Be Streamed Efficiently And Securely By The Mobile Users.

PROFILE-BASED CLUSTER EVOLUTION ANALYSIS- Identification Of Migration Patterns For Understanding Student Learning Behavior

Educational Process Mining Is One Of The Research Domains That Utilizes Students' Learning Behavior To Match Students' Actual Courses Taken And The Designed Curriculum. While Most Works Attempt To Deal With The Case Perspective (i.e., Traces Of The Cases), The Temporal Case Perspective Has Not Been Discussed. The Temporal Case Perspective Aims To Understand The Temporal Patterns Of Cases (e.g., Students' Learning Behavior In A Semester). This Study Proposes Modified Cluster Evolution Analysis, Called Profile-based Cluster Evolution Analysis, For Students' Learning Behavior Based On Profiles. The Results Show Three Salient Features: (1) Cluster Generation; (2) Within-cluster Generation; And (3) Time-based Between-cluster Generation. The Cluster Evolution Phase Modifies The Existing Cluster Evolution Analysis With A Dynamic Profiler. The Model Was Tested On Actual Educational Data Of The Information System Department In Indonesia. The Results Showed The Learning Behavior Of Students Who Graduated On Time, The Learning Behavior Of Students Who Graduated Late, And The Learning Behavior Of Students Who Dropped Out. Students Changed Their Learning Behavior By Observing The Migration Of Students From Cluster To Cluster For Each Semester. Furthermore, There Were Distinct Learning Behavior Migration Patterns For Each Category Of Students Based On Their Performance. The Migration Pattern Can Suggest To Academic Stakeholders To Understand About Students Who Are Likely To Drop Out, Graduate On Time Or Graduate Late. These Results Can Be Used As Recommendations To Academic Stakeholders For Curriculum Assessment And Development And Dropout Prevention.

INTERRUPTION DETECTION- Resourceful Interruption Detection With Blossom Filtering In Manager Area Networks

Due To Its Cost Efficiency The Controller Area Network (CAN) Is Still The Most Wide-spread In-vehicle Bus And The Numerous Reported Attacks Demonstrate The Urgency In Designing New Security Solutions For CAN. In This Work We Propose An Intrusion Detection Mechanism That Takes Advantage Of Bloom Filtering To Test Frame Periodicity Based On Message Identifiers And Parts Of The Data-field Which Facilitates Detection Of Potential Replay Or Modification Attacks. This Proves To Be An Effective Approach Since Most Of The Traffic From In-vehicle Buses Is Cyclic In Nature And The Format Of The Data-field Is Fixed Due To Rigid Signal Allocation. Bloom Filters Provide An Efficient Time-memory Tradeoff Which Is Beneficial For The Constrained Resources Of Automotive Grade Controllers. We Test The Correctness Of Our Approach And Obtain Good Results On An Industry-standard CANoe Based Simulation For A J1939 Commercial-vehicle Bus And Also On CAN-FD Traces Obtained From A Real-world High-end Vehicle. The Proposed Filtering Mechanism Is Straight-forward To Adapt For Any Other Time-triggered In-vehicle Bus, E.g., FlexRay, Since It Is Built On Time-driven Characteristics.

CYBER PHYSICAL SYSTEM- Minute Cyber-Physical Systems For Presentation Development In The Factory Of The Prospect

The Complexity And Dynamic Of The Manufacturing Environment Are Growing Due To The Changes Of Manufacturing Demand From Mass Production To Mass Customization That Require Variable Product Types, Small Lot Sizes, And A Short Lead-time To Market. Currently, The Automatic Manufacturing Systems Are Suitable For Mass Production. To Cope With The Changes Of The Manufacturing Environment, The Paper Proposes The Model And Technologies For Developing A Smart Cyber-physical Manufacturing System (Smart-CPMS). The Transformation Of The Actual Manufacturing Systems To The Smart-CPMS Is Considered As The Next Generation Of Manufacturing Development In Industry 4.0. The Smart-CPMS Has Advanced Characteristics Inspired From Biology Such As Self-organization, Self-diagnosis, And Self-healing. These Characteristics Ensure That The Smart-CPMS Are Able To Adapt With Continuously Changing Manufacturing Requirements. The Model Of Smart-CPMS Is Inherited From The Organization Of Living Systems In Biology And Nature. Consequently, In The Smart-CPMS, Each Resource On The Shop Floor Such As Machines, Robots, Transporters, And So On, Is An Autonomous Entity, Namely A Cyber-physical System (CPS) Which Is Equipped With Cognitive Capabilities Such As Perception, Reasoning, Learning, And Cooperation. The Smart-CPMS Adapts To The Changes Of Manufacturing Environment By The Interaction Among CPSs Without External Intervention. The CPS Implementation Uses The Cognitive Agent Technology. Internet Of Things (IoT) With Wireless Networks, Radio Frequency Identification (RFID), And Sensor Networks Are Used As Information And Communication Technology (ICT) Infrastructure For Carrying Out The Smart-CPMS.

ONLINE SOCIAL NETWORKS- Adaptive Diffusion Of Sensitive Information In Online Social Networks

The Cascading Of Sensitive Information Such As Private Contents And Rumors Is A Severe Issue In Online Social Networks. One Approach For Limiting The Cascading Of Sensitive Information Is Constraining The Diffusion Among Social Network Users. However, The Diffusion Constraining Measures Limit The Diffusion Of Non-sensitive Information Diffusion As Well, Resulting In The Bad User Experiences. To Tackle This Issue, In This Paper, We Study The Problem Of How To Minimize The Sensitive Information Diffusion While Preserve The Diffusion Of Non-sensitive Information, And Formulate It As A Constrained Minimization Problem Where We Characterize The Intention Of Preserving Non-sensitive Information Diffusion As The Constraint. We Study The Problem Of Interest Over The Fully-known Network With Known Diffusion Abilities Of All Users And The Semi-known Network Where Diffusion Abilities Of Partial Users Remain Unknown In Advance. By Modeling The Sensitive Information Diffusion Size As The Reward Of A Bandit, We Utilize The Bandit Framework To Jointly Design The Solutions With Polynomial Complexity In The Both Scenarios. Moreover, The Unknown Diffusion Abilities Over The Semi-known Network Induce It Difficult To Quantify The Information Diffusion Size In Algorithm Design. For This Issue, We Propose To Learn The Unknown Diffusion Abilities From The Diffusion Process In Real Time And Then Adaptively Conduct The Diffusion Constraining Measures Based On The Learned Diffusion Abilities, Relying On The Bandit Framework. Extensive Experiments On Real And Synthetic Datasets Demonstrate That Our Solutions Can Effectively Constrain The Sensitive Information Diffusion, And Enjoy A 40 Percent Less Diffusion Loss Of Non-sensitive Information Comparing With Four Baseline Algorithms.

PRIVATE KEY AGAINST MEMORY DISCLOSURE ATTACKS- Mimosa Protecting Private Keys Against Memory Disclosure Attacks Using Hardware Transactional Memory

Cryptography Is Essential For Computer And Network Security. When Cryptosystems Are Deployed In Computing Or Communication Systems, It Is Extremely Critical To Protect The Cryptographic Keys. In Practice, Keys Are Loaded Into The Memory As Plaintext During Cryptographic Computations. Therefore, The Keys Are Subject To Memory Disclosure Attacks That Read Unauthorized Data From RAM. Such Attacks Could Be Performed Through Software Exploitations, Such As OpenSSL Heartbleed, Even When The Integrity Of The Victim System's Binaries Is Maintained. They Could Also Be Done Through Physical Methods, Such As Cold-boot Attacks, Even If The System Is Free Of Software Vulnerabilities. This Paper Presents Mimosa, To Protect RSA Private Keys Against Both Software-based And Physical Memory Disclosure Attacks. Mimosa Uses Hardware Transactional Memory (HTM) To Ensure That (a) Whenever A Malicious Thread Other Than Mimosa Attempts To Read The Plaintext Private Key, The Transaction Aborts And All Sensitive Data Are Automatically Cleared With Hardware, Due To The Strong Atomicity Guarantee Of HTM; And (b) All Sensitive Data, Including Private Keys And Intermediate States, Appear As Plaintext Only Within CPU-bound Caches, And Are Never Loaded To RAM Chips. To The Best Of Our Knowledge, Mimosa Is The First Solution To Use Transactional Memory To Protect Sensitive Data Against Memory Attacks. However, The Fragility Of TSX Transactions Introduces Extra Cache-clogging Denial-of-service (DoS) Threats, And Attackers Could Sharply Degrade The Performance By Concurrent Memory-intensive Tasks. To Mitigate The DoS Threats, We Further Partition An RSA Private-key Computation Into Multiple Transactional Parts By Analyzing The Distribution Of Aborts, While (sensitive) Intermediate Results Are Still Protected Across Transactional Parts. Through Extensive Experiments, We Show That Mimosa Effectively Protects Cryptographic Keys Against Attacks That Attempt To Read Sensitive Data In Memory, And Introduce.

BI-MORPHING- A Bi-Directional Bursting Defense Against Website Fingerprinting Attacks

Network Traffic Analysis Has Been Increasingly Used In Various Applications To Either Protect Or Threaten People, Information, And Systems. Website Fingerprinting Is A Passive Traffic Analysis Attack Which Threatens Web Navigation Privacy. It Is A Set Of Techniques Used To Discover Patterns From A Sequence Of Network Packets Generated While A User Accesses Different Websites. Internet Users (such As Online Activists Or Journalists) May Wish To Hide Their Identity And Online Activity To Protect Their Privacy. Typically, An Anonymity Network Is Utilized For This Purpose. These Anonymity Networks Such As Tor (The Onion Router) Provide Layers Of Data Encryption Which Poses A Challenge To The Traffic Analysis Techniques. Although Various Defenses Have Been Proposed To Counteract This Passive Attack, They Have Been Penetrated By New Attacks That Proved The Ineffectiveness And/or Impracticality Of Such Defenses. In This Work, We Introduce A Novel Defense Algorithm To Counteract The Website Fingerprinting Attacks. The Proposed Defense Obfuscates Original Website Traffic Patterns Through The Use Of Double Sampling And Mathematical Optimization Techniques To Deform Packet Sequences And Destroy Traffic Flow Dependency Characteristics Used By Attackers To Identify Websites. We Evaluate Our Defense Against State-of-the-art Studies And Show Its Effectiveness With Minimal Overhead And Zero-delay Transmission To The Real Traffic.

FAULT DETECTION FILTER- Fault Detection Filter And Controller Co-Design For Unmanned Surface Vehicles Under DoS Attacks

This Paper Addresses The Co-design Problem Of A Fault Detection Filter And Controller For A Networked-based Unmanned Surface Vehicle (USV) System Subject To Communication Delays, External Disturbance, Faults, And Aperiodic Denial-of-service (DoS) Jamming Attacks. First, An Event-triggering Communication Scheme Is Proposed To Enhance The Efficiency Of Network Resource Utilization While Counteracting The Impact Of Aperiodic DoS Attacks On The USV Control System Performance. Second, An Event-based Switched USV Control System Is Presented To Account For The Simultaneous Presence Of Communication Delays, Disturbance, Faults, And DoS Jamming Attacks. Third, By Using The Piecewise Lyapunov Functional (PLF) Approach, Criteria For Exponential Stability Analysis And Co-design Of A Desired Observer-based Fault Detection Filter And An Event-triggered Controller Are Derived And Expressed In Terms Of Linear Matrix Inequalities (LMIs). Finally, The Simulation Results Verify The Effectiveness Of The Proposed Co-design Method. The Results Show That This Method Not Only Ensures The Safe And Stable Operation Of The USV But Also Reduces The Amount Of Data Transmissions.

FLOODING BASED DENIAL OF SERVICE- Modeling And Detection Of Flooding-Based Denial-of-Service Attack In Wireless Ad Hoc Network Using Bayesian Inference

Wireless Ad Hoc Networks Are Widely Useful In Locations Where The Existing Infrastructure Is Difficult To Use, Especially During The Situations Like Flood, Earthquakes, And Other Natural Or Man-made Calamities. Lack Of Centralized Management And Absence Of Secure Boundaries Make These Networks Vulnerable To Various Types Of Attacks. Moreover, The Mobile Nodes Used In These Networks Have Limited Computational Capability, Memory, And Battery Backup. Flooding-based Denial-of-service (DoS) Attack, Which Results In Denial Of Sleep Attack, Targets The Mobile Node's Constrained Resources Which Results In Excess Consumption Of Battery Backup. In SYN Flooding-based DoS Attack, The Attacker Sends A Large Number Of Spoofed SYN Packets Which Not Only Overflow The Target Buffer But Also Creates Network Congestion. The Present Article Is Divided Into Three Parts: 1) Mathematical Modeling For SYN Traffic In The Network Using Bayesian Inference; 2) Proving The Equivalence Of Bayesian Inference With Exponential Weighted Moving Average; And 3) Developing An Efficient Algorithm For The Detection Of SYN Flooding Attack Using Bayesian Inference. Based On The Comprehensive Evaluation Using Mathematical Modeling And Simulation, The Proposed Method Can Successfully Defend Any Type Of Flooding-based DoS Attack In Wireless Ad Hoc Network With Higher Detection Accuracy And Extremely Lower False Detection Rate.

SQL INJECTION ATTACK- A Detection And Prevention Technique On SQL Injection Attacks

With The Web Advancements Are Rapidly Developing, The Greater Part Of Individuals Makes Their Transactions On Web, For Example, Searching Through Data, Banking, Shopping, Managing, Overseeing And Controlling Dam And Business Exchanges, Etc. Web Applications Have Gotten Fit To Numerous Individuals' Day By Day Lives Activities. Dangers Pertinent To Web Applications Have Expanded To Huge Development. Presently A Day, The More The Quantity Of Vulnerabilities Will Be Diminished, The More The Quantity Of Threats Become To Increment. Structured Query Language Injection Attack (SQLIA) Is One Of The Incredible Dangers Of Web Applications Threats. Lack Of Input Validation Vulnerabilities Where Cause To SQL Injection Attack On Web. SQLIA Is A Malicious Activity That Takes Negated SQL Statement To Misuse Data-driven Applications. This Vulnerability Admits An Attacker To Comply Crafted Input To Disclosure With The Application's Interaction With Back-end Databases. Therefore, The Attacker Can Gain Access To The Database By Inserting, Modifying Or Deleting Critical Information Without Legitimate Approval. The Paper Presents An Approach Which Detects A Query Token With Reserved Words-based Lexicon To Detect SQLIA. The Approach Consists Of Two Highlights: The First One Creates Lexicon And The Second Step Tokenizes The Input Query Statement And Each String Token Was Detected To Predefined Words Lexicon To Prevent SQLIA. In This Paper, Detection And Prevention Technologies Of SQL Injection Attacks Are Experimented And The Result Are Satisfactory.

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