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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.

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.

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.

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.

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.

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.

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