Increasing the throughput rate against spectrum sensing data falsification attacks in cognitive radio networks

Cognitive Radio (CR) networks provide dynamic spectrum access and can significantly improve spectrum efficiency. Collaborative Spectrum Sensing (CSS) leverages spatial diversity among CR users to improve detection accuracy. However, in a realistic scenario, trusted CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack. In an SSDF attack, some malicious CR user reports deliberately falsified local sensing results to a data collector or Fusion Center (FC), which then influences the sensing decision. In this work, we investigate an analytical model for an SSDF attack and propose a robust defense strategy against such an attack. We show that learning and prediction methods can be applied to obtain FC's attack parameters and use a better defense strategy. We also assume a wireless environment with log-normal shadow damping and discuss the attack parameters that may affect the strength of the SSDF attack. Simulation results show the effectiveness of the proposed defense method against SSDF attacks, especially in cases where malicious users are in the majority.