Malicious Users Detection and Nullifying their Effects on Cooperative Spectrum Sensing

Abstract

Abstract Submitted：October 27, 2015 1 st Revision：March 20, 2016 Accepted：March 24, 2016* This study was supported by research fund from Chosun University, 2015.** Dept. of Information and Communication Engineering, Chosun Univ.*** Dept. of Information and Communication Engineering, Chosun Univ, Corresponding AuthorSpectrum sensing in cognitive radio (CR) has a great role in or der to utilize idle spectrum opportunistically, since it is responsible for making available dynamic spectrum access efficiently. In this research area, collaboration among multiple cognitive radio users has been proposed for the better ment of detection reliability. Even though cooperation among them improves the spectrum sensing performance, some fals ely reporting malicious users may degrade the performance rigorously. In this article, we have studied the de tection and nullifying the harmful effects of such malicious users by applying some well known outlier detection methods bas ed on Grubb’s test, Boxplot method and Dixon’s test in cooperative spectrum sensing. Initially, the performance of each technique is compared and found that Boxplot method outperforms both Grubb’s and Dixon’s test for the case w here multiple malicious users are present. Secondly, a new algorithm based on reputation and weight is developed to identify malicious users and cancel out their negative impact in final decision making. Simulation results demonstrate that the proposed scheme effectively identifies the malicious users and suppress their harmful effects at the fusio n center to decide whether the spectrum is idle.