Detection of collaborative SSDF attacks using abnormality detection algorithm in cognitive radio networks

Cognitive radio is a revolutionary paradigm to improve the utilization of scarce radio spectrum resources. In cognitive radio networks, collaborative spectrum sensing is considered as an effective method to improve the performance of primary user detection. However, collaborative spectrum sensing is vulnerable to spectrum sensing data falsification (SSDF) attacks, where malicious secondary users (attackers) send manipulated local sensing results to the fusion center. We find that malicious users can imitate honest users´ statistical characteristics by collaborating while launching attacks. We call this kind of attack as balanced collaborative (BC) attack. BC attackers can pass trusted nodes assistance methods which are very often used in existing secure schemes. Based on the theoretical analysis that the reports between BC attackers have the highest similarities, we propose an abnormality detection algorithm to detect BC attackers. The only information we need to know is the bit error probability on secondary users´ reporting channel. Numerical simulation results show that the proposed scheme can identify and weed out BC attackers accurately.