An Enhanced D-S Theory Cooperative Spectrum Sensing Algorithm against SSDF Attack

The current D-S theory cooperative spectrum sensing algorithms think all cognitive users are honest and don´t consider malicious user existing. When a malicious user falsifies its local sensing result and sends error data to the data fusion center, it will damage the cooperative spectrum sensing performance badly, which is called spectrum sensing data falsification (SSDF) attack. Due to the difference between the malicious user´s evidence and other cognitive users´, we use the similarity degree to calculate the reliability of evidence and propose a detection method against SSDF attack. The data fusion center removes the evidence with lower reliability and combines reliable evidences which is considered to been sent by honest cognitive users. As simulation results shown, the proposed method defends against the SSDF attack effectively and enhances the robustness of the D-S theory cooperative spectrum sensing algorithm.