A Reputation-Based Evaluation Method to Defend Against Massive SSDF Attacks

Cooperative spectrum sensing (CSS) is susceptible to spectrum sensing data falsification (SSDF) attacks in cognitive radio networks (CRN), which affects its sensing performance. This letter mainly focuses on the capability of accurately detecting malicious users (MUs) under SSDF attacks. For this purpose, a new reputation value evaluation method is proposed. The method obtains direct and indirect reputation values from the local sensing results of each secondary user (SU) and then evaluates their final reputation values through a weighted approach. In this way, the obtained final reputation values can be used to identify malicious users (MUs) among SUs. Simulation results demonstrate the validity of the proposed method against massive SSDF attacks.