Adversary detection for cognitive radio networks. (2018)

• Record Type: Book
• Further Information: Note: Xiaofan He, Huaiyu Dai.
• Authors: He, Xiaofan; Dai, Huaiyu, 1973-
• Contents: Intro; Preface; Contents; 1 Introduction; 1.1 Cognitive Radio Networks and Spectrum Sensing; 1.2 Overview of Security Vulnerabilities in CR Networks; 1.2.1 The PUE Attack; 1.2.2 The Byzantine Attack; 1.3 Summary; References; 2 Preliminaries of Analytical Tools; 2.1 Introduction; 2.2 Statistical Tools; 2.2.1 Sequential Hypothesis Testing; 2.2.2 Belief Propagation; 2.3 Machine Learning Tools; 2.3.1 Non-parametric Bayesian Classification; 2.3.2 Artificial Neural Networks; 2.3.3 Affinity Propagation; 2.4 Summary; References; 3 Overview of Adversary Detection in CR Networks; 3.1 Introduction 3.2 PUE Attack Detection3.2.1 Localization Based Approaches; 3.2.1.1 Wireless Sensor Network Assisted Approach; 3.2.1.2 Joint Localization Approach; 3.2.2 Statistical Analysis Based Approaches; 3.2.2.1 Sequential Hypothesis Testing Approach; 3.2.2.2 Belief Propagation Based PUE Attack Detection; 3.2.2.3 Anomaly Behavior Detection; 3.2.3 Physical Layer Approaches; 3.2.3.1 Feature Based Classification Approach; 3.2.3.2 Wireless Feature Based Detection; 3.2.3.3 Channel-Based PUE Attack Detection; 3.2.3.4 Embedding Cryptographic Signatures; 3.2.3.5 Signal Watermarking 3.2.4 Machine Learning Approach3.2.5 Other Defense Against PUE Attack; 3.3 Byzantine Attack Detection; 3.3.1 Reputation Based Detection; 3.3.1.1 Suspicious Level; 3.3.1.2 Outlier Factor; 3.3.1.3 Hierarchical Outlier Detection; 3.3.1.4 Trusted Sensor Assisted Reputation Mechanism; 3.3.1.5 Point System Based Detection; 3.3.2 Statistical Approach; 3.3.2.1 Statistical Behavior Comparison Based Approach; 3.3.2.2 Anomaly Behavior Detection; 3.3.2.3 Statistical Tests Based Approach; 3.3.2.4 Sequential Probability Ratio Test; 3.3.2.5 State Estimation Assisted Detection 3.3.2.6 Exploiting Temporal Correlation3.3.2.7 Correlation Based Filtering; 3.3.2.8 Goodness-of-Fit Test; 3.3.2.9 Belief Propagation Based Detection; 3.3.2.10 Dempster-Shafer Theory Based Byzantine Attack Detection; 3.3.3 Machine Learning Approaches; 3.3.3.1 No-Regret Learning; 3.3.3.2 Classification Based Detection; 3.3.4 Other Defense Against Byzantine Attack; 3.4 Summary; References; 4 Case Study I: Link Signature Assisted PUE Attack Detection; 4.1 Introduction; 4.2 Background on Link Signature; 4.3 Authenticate the PU Signal at the Helper Node; 4.4 PUE Attack Detection; 4.5 Summary
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2018
• Extent: 1 online resource
• Subjects: 621.382; Engineering; Cognitive radio networks; Radio frequency allocation; TECHNOLOGY & ENGINEERING / Mechanical; Cognitive radio networks; Radio frequency allocation; Computers -- Security -- General; Computer security; Telecommunication; Data protection; Technology & Engineering -- Telecommunications; Communications engineering / telecommunications; Electronic books
• Languages: English
• ISBNs: 9783319758688; 3319758683
• Related ISBNs: 9783319758671; 3319758675
• Notes: Note: Includes bibliographical references.; Note: Online resource; title from PDF title page (SpringerLink, viewed March 19, 2018).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.