Adversary Detection For Cognitive Radio Networks

Name: Adversary Detection For Cognitive Radio Networks
Brand: Springer
Price: 53.49 EUR
Availability: OnlineOnly

Xiaofan He Huaiyu Dai(Autor*in)

Springer (Verlag)

Erschienen am 7. März 2018

X, 74 Seiten

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978-3-319-75868-8 (ISBN)

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Beschreibung

This SpringerBrief provides a comprehensive study of the unique security threats to cognitive radio (CR) networks and a systematic investigation of the state-of-the-art in the corresponding adversary detection problems. In addition, detailed discussions of the underlying fundamental analytical tools and engineering methodologies of these adversary detection techniques are provided, considering that many of them are quite general and have been widely employed in many other related fields.

The exposition of this book starts from a brief introduction of the CR technology and spectrum sensing in Chapter 1. This is followed by an overview of the relevant security vulnerabilities and a detailed discussion of two security threats unique to CR networks, namely, the primary user emulation (PUE) attack and the Byzantine attack.

To better prepare the reader for the discussions in later chapters, preliminaries of analytic tools related to adversary detection are introduced in Chapter 2. In Chapter 3, a suite of cutting-edge adversary detection techniques tailor-designed against the PUE and the Byzantine attacks are reviewed to provide a clear overview of existing research in this field.

More detailed case studies are presented in Chapters 4 - 6. Specifically, a physical-layer based PUE attack detection scheme is presented in Chapter 4, while Chapters 5 and 6 are devoted to the illustration of two novel detection techniques against the Byzantine attack. Concluding remarks and outlooks for future research are provided in Chapter 7.

The primary audience for this SpringerBrief include network engineers interested in addressing adversary detection issues in cognitive radio networks, researchers interested in the state-of-the-art on unique security threats to cognitive radio networks and the corresponding detection mechanisms. Also, graduate and undergraduate students interested in obtaining comprehensive information on adversary detection in cognitive radio networks and applying the underlying techniques to address relevant research problems can use this SpringerBrief as a study guide.

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Inhalt

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 Detection
3.2.1 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 Approach
3.2.5 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 Correlation
3.3.2.7 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
References
5 Case Study II: HMM-Based Byzantine Attack Detection
5.1 Introduction
5.2 HMM-Based Spectrum Sensing Behavioral Model
5.2.1 Behavioral Model of Honest SUs
5.2.2 Behavioral Model of Byzantine Attackers
5.2.3 The Two-HMM Model
5.3 A Multi-HMM Inference Algorithm
5.4 HMM-Based Byzantine Attack Detection
5.5 Performance Evaluation
5.6 Summary
References
6 Case Study III: CFC-Based Byzantine Attack Detection
6.1 Introduction
6.2 System Model and the CFC Statistics
6.3 Using the CFC Statistics to Detect Byzantine Attacks
6.4 Performance Evaluation
6.5 Summary
References
7 Conclusion and Future Work
7.1 Summary
7.2 Future Works

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