Adaptive Learning of Byzantines' Behavior in Cooperative Spectrum Sensing

Aditya Vempaty, Keshav Agrawal, Hao Chen, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

Abstract

This paper considers the problem of Byzantine attacks on cooperative spectrum sensing in cognitive radio networks. Our major contribution is a technique to learn about the cognitive radio (CR) potential malicious behavior over time and thereby identifies the Byzantines and then estimates their probabilities of false alarm (Pf a ) and detection (P D ). We show that for a given set of data over time, the Byzantines can be identified for any a (percentage of Byzantines). It has also been shown that these estimates of Pf a and Pn of the Byzantines are asymptotically unbiased and converge to their true values at the rate of O(T -1/2 ). We then use these probabilities to adaptively design the fusion rule. We calculate the Probability of error (Q e ) and compare it with the minimum probability of error possible.

Original languageAmerican English
JournalElectrical and Computer Engineering Faculty Publications and Presentations
DOIs
StatePublished - 28 Mar 2011

EGS Disciplines

  • Electrical and Computer Engineering

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