Abstract
The multi-HMM inference algorithm presented in the previous chapter can effectively assist the Byzantine attack detection when either the percentage of the malicious SUs or their flipping probability is not too high. To further enhance the detection performance, a tailor-designed Byzantine attack detection scheme, termed CFC, will be presented in this chapter. In this method, two natural yet effective CFC statistics that can capture the second-order properties of the underlying spectrum dynamics and the SUs spectrum sensing behaviors are constructed for Byzantine attacker identification. More specifically, we will first briefly clarify the underlying system model and then presents the CFC based Byzantine attack detection algorithm. In addition, performance analysis of this method will also be presented.
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Notes
- 1.
- 2.
Note that we do not consider the case that both φ 01 and φ 10 are zeros. Because in that case, the malicious SUs actually reduce to the honest SUs, and there is no need to detect them.
- 3.
Other than the majority voting rule, the fusion center may also use the AND rule [14], where the spectrum is decided to be occupied only if all the SUs report so. Another possible choice is the OR rule [15], where the spectrum will decided to be occupied as long as there is one SU reports so. As it can be seen, the AND rule is aggressive and the OR rule is conservative while the majority voting rule considered here is somewhere in between.
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He, X., Dai, H. (2018). Case Study III: CFC-Based Byzantine Attack Detection. In: Adversary Detection For Cognitive Radio Networks. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-75868-8_6
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