The cognitive radio network (CRN) is vulnerable to various newly-arising attacks targeting the weaknesses of cognitive radio (CR) communication and networking. In this paper, we focus on improving the secrecy performance of CR communications in a decentralized, multiple-channel manner while various eavesdroppers (EVs) try to listen to their private information. By choosing the best channel, the secondary user (SU) aims at mitigating the effects of eavesdropping and other SUs that compete for the same channel. Accordingly, the problem of finding the best channel that maximizes the secrecy rate for the SU is formulated as the framework of multiple repeated games where both the SU and the EVs try to maximize their own performance. In this case, the secrecy rate of an SU is defined based on the expected rewards of the SUs and the EVs. In the paper, we propose a repeated games-based scheme that can provide the best channel for the SU to avoid eavesdropping attacks and also minimize interference from other SUs that compete for the same channel. The simulation results demonstrate that the proposed scheme can combat a physical layer attack from EVs quite well and can provide much better performance, in comparison with other conventional channel selection schemes.
Learning from Private Information in Noisy Repeated Games
