Robust Cooperative Spectrum Sensing against Probabilistic SSDF Attack in Cognitive Radio Networks

Abstract

Cooperative spectrum sensing is one of the key technologies to accurately detect the primary user (PU) activity in cognitive radio networks (CRNs). However, collaboration among multiusers provides malicious users (MUs) with an opportunity to launch spectrum sensing data falsification (SSDF) attack. Various approaches have been proposed regarding how to mitigate the negative effect of SSDF attack, while extensive references have strong assumptions such as MUs are in minority and need more decision samples. In this paper, we develop a general SSDF attack model. We further propose a robust data fusion scheme, named robust weighted sequential probability ratio test (RWSPRT), which can deal with various attack probabilities. In the proposed RWSPRT, according to the correct decision ability, the reputation value (RV) of each SU is integrated into weight coefficient of weighted sequential probability ratio test (WSPRT) to improve the performance of cooperative spectrum sensing. Simulation results show that RWSPRT performs more robust than traditional data fusion techniques whereas requires less number of samples, even when a large number of MUs exists in CRNs.