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.