Secure Collaborative Spectrum Sensing for Distributed Cognitive Radio Networks

Abstract

Spectrum sensing is a key function of Cognitive Radio (CR) networks. An accurate spectrum sensing scheme can improve spectrum utilization. But, in practice, detection performance is often degraded with multipath fading, shadowing and receiver uncertainty issues. To overcome the impact of these issues, Collaborative Spectrum Sensing (CSS) has been shown to be an effective approach to improve the detection performance by exploiting diversity. The reliability of CSS can be severely degraded by Spectrum Sensing Data Falsification (SSDF) attacks. Protecting the CR networks against SSDF attacks, Weighted Sequential Probability Ratio Test (WSPRT) has been proposed. Compared with conventional SPRT, the WSPRT improves correct sensing probability at the cost of increasing sampling overhead. In the present study, weighted majority rule is introduced and combined with the WSPRT to improve trustworthiness of collaborative spectrum sensing in the presence of SSDF attackers. Furthermore, to avoid increasing the sampling overhead, Roulette Wheel Selection (RWS) algorithm is used to collaborative node selection. The proposed method is called Developed WSPRT (DWSPRT). Simulation results show that the DWSPRT is an effective data fusion approach against SSDF attacks, especially for CR networks located in hostile environments.