Detection of Misbehavior in Cooperative Spectrum Sensing

The problem of cooperative spectrum sensing in the presence of multiple classes of honest and misbehaving cognitive radios (CRs) is investigated. The CRs transmit their binary decisions regarding the state of the channel to a fusion center (FC) which must classify the CRs and determine whether the channel is vacant of the primary user. We present a novel approach based on the expectation maximization (EM) algorithm in order to detect the presence of the primary user, to classify the cognitive radios, and to compute their detection and false alarm probabilities. In contrast to reputation-based classifiers (RBCs), our approach can classify the radios into more than two classes of honest and malicious CRs. Numerical results show significant improvements over RBC. In particular, with only a few decisions from the CRs, the proposed algorithm can quickly and efficiently classify the CRs whereas RBC fails in many cases even for networks with a large number of honest CRs.