Adaptive learning of Byzantines' behavior in cooperative spectrum sensing

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.