CatchIt: Detect Malicious Nodes in Collaborative Spectrum Sensing

Collaborative spectrum sensing in cognitive radio networks has been proposed as an efficient way to improve the performance of primary users detection. In collaborative spectrum sensing schemes, secondary users are often assumed to be trustworthy. In practice, however, cognitive radio nodes can be compromised. Compromised secondary users can report false detection results and significantly degrade the performance of spectrum sensing. In this paper, we investigate the case that there are multiple malicious users in cognitive radio networks and the exact number of malicious users is unknown. An onion-peeling approach is proposed to defense against multiple untrustworthy secondary nodes. We calculate suspicious level of all nodes according to their reports. When the suspicious level of a node is beyond certain threshold, it will be considered as malicious and its report will be excluded in decision-making. We continue to calculate the suspicious level of remaining nodes until no malicious node can be found. Simulation results show that malicious nodes greatly degrade the performance of collaborative sensing, and the proposed scheme can efficiently detect malicious nodes. Compared with existing defense methods, the proposed scheme significantly improves the performance of primary user detection, measured by ROC curves, and captures the dynamic change in the behaviors of malicious users.