Robust cooperative spectrum sensing based on adaptive reputation and evidential reasoning theory in cognitive radio network

In this paper we investigate the problem of cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs) in the presence of malicious attacks from secondary users (SUs). We propose a novel CSS approach based on a new adaptive reputation and evidential reasoning theory (ERT) to detect the malicious SUs, to strengthen the sensing ability, and to keep good stability and generalization ability. Different from the previous work, we assume that the fusion center has no prior information of the radios in CRNs except the condition that the honest SUs are in majority. Specifically, to counter different attack strategies, we present an adaptive reputation which fully considers the signal-to-noise ratio, historical sensing data information, and current local sensing results via an energy detection method. The proposed adaptive reputation can effectively identify the malicious SUs when the CRNs are with single-attack or multiple-attack models. Furthermore, we present a CSS scheme via the proposed adaptive reputation and ERT on the basis of local sensing results derived from the energy detection method. The new CSS scheme has good robustness and generality and can decrease the computational complexity and overcome the evidence conflict problems of Dempster–Shafer theory-based CSS schemes. Extensive simulations are carried out to demonstrate the performance and the superiority of the proposed approach in contrast with previous methods