Adversary detection for cognitive radio networks / Xiaofan He, Huaiyu Dai.
He, Xiaofan, author.; Dai, Huaiyu, 1973- author.
2018
TK5103.4815
Available Online

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Title Adversary detection for cognitive radio networks / Xiaofan He, Huaiyu Dai.
Author He, Xiaofan, author.
ISBN 9783319758688 (electronic book)
3319758683 (electronic book)
9783319758671
3319758675
DOI https://doi.org/10.1007/978-3-319-75868-8
Published Cham, Switzerland : Springer, 2018.
Language English
Description 1 online resource.
Other Standard Identifiers 10.1007/978-3-319-75868-8 doi
Call Number TK5103.4815
Dewey Decimal Classification 621.382
Summary 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. .
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 19, 2018).
Added Author Dai, Huaiyu, 1973- author.
Series SpringerBriefs in electrical and computer engineering.
Available in Other Form Print version: 9783319758671
Linked Resources Online Access
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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

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