Defense Against SSDF Attack in Cognitive Radio Networks: Attack-Aware Collaborative Spectrum Sensing Approach

Abstract

The reliability of the collaborative spectrum sensing (CSS) can be severely decreased by spectrum sensing data falsification (SSDF) attacks. In an SSDF attack, some malicious users intentionally report incorrect local sensing results to the fusion center (FC) and disrupt the global decision-making process. The present study introduces a new defense scheme called attack-aware CSS (ACSS). The proposed method estimates attack strength and applies it in the $k {-} \text{out} {-} N$ rule to obtain the optimum value of $k$ that minimizes the Bayes risk. The attack strength is defined as the ratio of the number of malicious users to the total number of users, which is equal to the probability that a specific user is malicious.