Nullifying malicious users for cooperative spectrum sensing in cognitive radio networks using outlier detection methods

Abstract

A number of cooperative spectrum sensing techniques have been pur- posed in cognitive radio networks. However, collaboration between multiple cog- nitive radio (CR) users also raises a number of security issues. It has been shown that the cooperative gain can be severely affected by malfunctioning or malicious CR users in cooperative sensing. One of them is spectrum sensing data falsification (SSDF) attack, where malicious users transmit false information instead of real detection results and thereby affecting the final decision. In this paper, we study the detection and suppressing the malicious users using different outlier detection methods based on Grubb’s test, Boxplot method and Dixon’s test. We have com- pared their performance through simulation and receiver operating characteristics (ROC) curve shows that Boxplot method outperforms both Grubb’s and Dixon’s test for the case where multiple malicious users are present.