Securing Collaborative Spectrum Sensing Against Malicious Attackers in Cognitive Radio Networks

Abstract

Collaborative spectrum sensing (CSS) has been suggested to overcome the destructive effect of multipath fading, shadowing, and receiver uncertainty. But, in practice, the reliability of the CSS can be severely decreased by spectrum sensing data falsification (SSDF) attacks. In an SSDF attack, some malicious users intentionally report falsified local sensing results to the data collector or fusion center and significantly degrade the CSS performance. As a countermeasure against SSDF attack, we introduce a new defense method called attack-aware CSS (ACSS). The proposed ACSS method estimates the credit value of each cognitive radio user and identifies the malicious attackers along with their attack strategies. To do this, the innovated method allocates an appropriate collaborative weight for each user and improves the CSS performance. We evaluate the performance of the ACSS by comparing it with conventional likelihood ratio test (LRT) and weighted sequential probability ratio test (WSPRT) under various number of malicious. Furthermore, the practical limitation issues that need to be considered when applying the ACSS technique are discussed. Simulation results show the effectiveness of the proposed method for defense against SSDF attacks compared with conventional LRT and WSPRT.