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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.