Mitigating SSDF attack using distance-based outlier approach in cognitive radio networks

Collaborative spectrum sensing is employed in cognitive radio networks for improving the spectrum sensing accuracy. The collaborating cognitive radios send their individual sensing results to the fusion center (FC) which aggregates the results to come to a final sensing decision. Malicious radios may adversely influence the final sensing decision by transmitting false spectrum sensing results to the FC. This attack is commonly known as the spectrum sensing data falsification (SSDF) attack. Hence, in the light of such a threat, it is pertinent for the FC to identify any such malicious radios, if any and isolate them from the decision process. In this paper, a distance-based outlier detection approach is proposed which mines the sensing reports at the FC for detection and isolation of such malicious users. Numerical simulations results support the validity of the proposed approach.