Bayesian-Inference-Based Sliding Window Trust Model Against Probabilistic SSDF Attack in Cognitive Radio Networks

Abstract

Collaborative spectrum sensing as a promising approach is widely investigated for identifying available spectrum in cognitive radio networks. However, such cooperation is vulnerable to be threatened by spectrum sensing data falsification (SSDF) attacks. Many previous attacker detection schemes rely on some strong assumptions, such as prior knowledge of the local performance of each secondary user (SU) and higher attack probability. To overcome these drawbacks, in this article, we develop a Bayesian-inference-based sliding window trust model to identify and weed out independent and collaborative probabilistic SSDF attackers without any prior knowledge of the attack behaviors. Based on this model, the trust value of each SU is obtained by combining the weighted intermediate sensing information from multiple small sliding windows. A sigmoid-log-function-based scheme is introduced to mitigate trust value fluctuations caused by different attacking probabilities. Finally, extensive simulation results show that compared with existing schemes, the proposed approach can identify probabilistic SSDF attackers effectively and isolate them with a higher detection rate across a wide range of attacking scenarios.