A Byzantine Attack Defender in Cognitive Radio Networks: The Conditional Frequency Check

Security concerns are raised for collaborative spectrum sensing due to its vulnerabilities to the potential attacks from malicious secondary users. Most existing malicious user detection methods are reputation-based, which become incapable when the malicious users dominate the network. On the other hand, although Markovian models characterize the spectrum state behavior more precisely, there is a scarcity of malicious user detection methods which fully explore this feature. In this paper, a new malicious user detection method using two proposed conditional frequency check (CFC) statistics is developed under the Markovian model for the spectrum state. With the assistance of one trusted user, the proposed method can achieve high malicious user detection accuracy (≥ 95%) for arbitrary percentage of malicious users that may even be equipped with more advanced sensing devices, and can thus improve the collaborative spectrum sensing performance significantly. Simulation results are provided to verify the theoretical analysis and effectiveness of the proposed method.