Joint Spectrum Sensing and Detection of Malicious Nodes via Belief Propagation

In this paper we address the problem of statistical spectrum sensing attacks, where misbehaving nodes falsify their sensing reports with a certain probability in order to artificially increase or reduce the throughput of a cognitive network. Instead of trying to identify unreliable nodes and exclude them from the decision process, we propose a novel approach where spectrum sensing and estimation of type/probability of the attacks are performed jointly. Our method is based on a Bayesian formulation and is implemented using belief propagation on factor graphs. The performance of the proposed method is then evaluated by analytical results and by simulations.