A Collaborative Spectrum Sensing Algorithm Based on Reputation Update against Malicious User Attacks

Abstract

In cognitive radio networks, collaborative spectrum sensing (CSS) algorithms could improve spectrum detection performance; however, most explorations are based on reliable network environments. In the real network environment, there may be malicious users that bring wrong spectrum sensing results and attacks designed by them remarkably reduce the spectrum efficiency. In order to resist the attacks of malicious users, this paper proposes a CSS method based on the reputation update. By setting an appropriate reputation threshold, the user fusion center selects the sensing user with a higher reputation to participate in the CSS. Each user’s reputation value is then updated according to whether its local sensing result matches the final judgment result. This article chiefly discusses scenarios for application of three information fusion rules. The simulation results reveal that the proposed approach with reputation update outperforms the conventional CSS algorithm for a variety of judgment rules. The proposed algorithm is capable of preventing lower reputation users from participating in the CSS, filtering out malicious users, and eliminating the impact of malicious users’ attacks.