Reputation-based Likelihood Ratio Test with anchor nodes assistance

Abstract

Existing Collaborative Spectrum Sensing (CSS) algorithms in the presence of Spectrum Sensing Data Falsification (SSDF) attacks have been investigated in the small-scale attacks, where malicious users are assumed to be in a minority and have limited effects on final decision. But, in massive attacks, where there are a large number of malicious users, the final decision is unreliable and existing methods have low effectiveness. In contrary, we propose a new Weighted Likelihood Ratio Test (WLRT) that collaborative weight is calculated by comparing the sensing history of each user with the reliable anchor nodes' global decision. The obtained weights are applied in LRT to improve the CSS performance. Simulation results verify the effectiveness of the proposed method.