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Adversary Detection For Cognitive Radio NetworksConclusion and Future Work

Adversary Detection For Cognitive Radio Networks: Conclusion and Future Work [After reviewing relevant background and preliminaries in Chaps. 1 and 2, our discussion sets off from an overview of the state-of-the-art of adversary detection techniques against the PUE attack and the Byzantine attack in Chap. 3. In the subsequent chapters, more detailed case studies of several adversary detection schemes are conducted. Specifically, a link signature assisted PUE attack detection scheme is discussed in Chap. 4. In Chap. 5, an HMM-based Byzantine detection scheme is introduced. In this approach, the adversary is detected by inspecting the parameter difference in the corresponding HMM models for the honest SUs and the adversary. In Chap. 6, a CFC based Byzantine attack detection algorithm was presented. In this approach, two CFC statistics are extracted from the SUs spectrum sensing behaviors and then compared with those of a trusted SU for adversary detection. Lastly, concluding remarks and outlooks for future works are provided in this chapter.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adversary Detection For Cognitive Radio NetworksConclusion and Future Work

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Publisher
Springer International Publishing
Copyright
© The Author(s) 2018
ISBN
978-3-319-75867-1
Pages
73 –74
DOI
10.1007/978-3-319-75868-8_7
Publisher site
See Chapter on Publisher Site

Abstract

[After reviewing relevant background and preliminaries in Chaps. 1 and 2, our discussion sets off from an overview of the state-of-the-art of adversary detection techniques against the PUE attack and the Byzantine attack in Chap. 3. In the subsequent chapters, more detailed case studies of several adversary detection schemes are conducted. Specifically, a link signature assisted PUE attack detection scheme is discussed in Chap. 4. In Chap. 5, an HMM-based Byzantine detection scheme is introduced. In this approach, the adversary is detected by inspecting the parameter difference in the corresponding HMM models for the honest SUs and the adversary. In Chap. 6, a CFC based Byzantine attack detection algorithm was presented. In this approach, two CFC statistics are extracted from the SUs spectrum sensing behaviors and then compared with those of a trusted SU for adversary detection. Lastly, concluding remarks and outlooks for future works are provided in this chapter.]

Published: Mar 8, 2018

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