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Adversary Detection For Cognitive Radio NetworksCase Study II: HMM-Based Byzantine Attack Detection

Adversary Detection For Cognitive Radio Networks: Case Study II: HMM-Based Byzantine Attack... [Most of the existing defense schemes against the Byzantine attack discussed in Chap. 3 either assume that the underlying spectrum states at different timeslots are independent or they only focus on the measurements collected in a single timeslot. Nonetheless, for many practical scenarios, the activities of the PUs and the induced spectrum states often follow a Markov process, and hence the spectrum sensing behaviors of the SUs may be better characterized by the HMM. Under this modeling, a novel HMM-based Byzantine attack detection technique can be developed to enforce the robustness of collaborative spectrum sensing. To illustrate this, the HMM-based spectrum sensing behavioral model is presented first, and based on which, the sought-after multi-HMM inference algorithm is introduced. Then, the overall HMM-based Byzantine attack detection scheme is demonstrated along with some numerical results to corroborate its effectiveness.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adversary Detection For Cognitive Radio NetworksCase Study II: HMM-Based Byzantine Attack Detection

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Publisher
Springer International Publishing
Copyright
© The Author(s) 2018
ISBN
978-3-319-75867-1
Pages
51 –62
DOI
10.1007/978-3-319-75868-8_5
Publisher site
See Chapter on Publisher Site

Abstract

[Most of the existing defense schemes against the Byzantine attack discussed in Chap. 3 either assume that the underlying spectrum states at different timeslots are independent or they only focus on the measurements collected in a single timeslot. Nonetheless, for many practical scenarios, the activities of the PUs and the induced spectrum states often follow a Markov process, and hence the spectrum sensing behaviors of the SUs may be better characterized by the HMM. Under this modeling, a novel HMM-based Byzantine attack detection technique can be developed to enforce the robustness of collaborative spectrum sensing. To illustrate this, the HMM-based spectrum sensing behavioral model is presented first, and based on which, the sought-after multi-HMM inference algorithm is introduced. Then, the overall HMM-based Byzantine attack detection scheme is demonstrated along with some numerical results to corroborate its effectiveness.]

Published: Mar 8, 2018

Keywords: Byzantine Attackers; Single Timeslot; State Spectrum; Fusion Center; Malicious Ones

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