Fast Detection of Malicious Behavior in Cooperative Spectrum Sensing

In this paper we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving nodes. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. In contrast to previous work we assume that the FC has no prior information about the radios in the network except that the honest radios are in majority. As shown in the paper this is required for any algorithm to uniquely identify the CRs. Another distinguishing feature is that our approach can classify the radios into more than just two classes of honest and malicious CRs. This applies in cases where the honest CRs have different detection and false alarm probabilities, which may arise when they employ different spectrum sensing techniques or encounter dissimilar channel and noise conditions. Another case is when the CRN includes more than one type of misbehaving CRs. Our numerical results show significant improvements over the widely popular reputation-based classifier (RBC). In particular, with only a few decisions from the CRs, the proposed algorithm can quickly and efficiently classify the CRs whereas the RBC method fails even for networks with a large number of CRs. In all of our numerical results the EM algorithm converged in five or fewer iterations resulting in fast convergence of the proposed method. This makes the proposed method a good candidate for implementation in CRNs. The numerical results are also compared with the Cramer-Rao lower bound and show a close match. Simulation results are also presented to demonstrate the efficacy of the proposed algorithm in the presence of correlated observations among the radios.