Attack-Proof Collaborative Spectrum Sensing in Cognitive Radio Networks

Collaborative sensing in cognitive radio networks can significantly improve the probability of detecting the transmission of primary users. In current collaborative sensing schemes, all collaborative secondary users are assumed to be honest As a consequence, the system is vulnerable to attacks in which malicious secondary users report false detection results. In this paper, we investigate how to improve the security of collaborative sensing. Particularly, we develop a malicious user detection algorithm that calculates the suspicious level of secondary users based on their past reports. Then, we calculate trust values as well as consistency values that are used to eliminate the malicious users' influence on the primary user detection results. Through simulations, we show that even a single malicious user can significantly degrade the performance of collaborative sensing. The proposed trust value indicator can effectively differentiate honest and malicious secondary users. The receiver operating characteristic (ROC) curves for the primary user detection demonstrate the improvement in the security of collaborative sensing.