Malicious Users Detection and Nullifying their Effects on Cooperative Spectrum Sensing in Cognitive Radio Networks

Abstract

ABSTRACT

Malicious Users Detection and Nullifying their Effects on Cooperative Spectrum Sensing in Cognitive Radio Networks

Prakash Prasain Advisor: Prof. Dong-You Choi, Ph.D. Department of Information and Communication Engineering, Graduate School of Chosun University

Spectrum sensing in cognitive radio (CR) has a great role in order 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 betterment of detection reliability. Even though cooperation among them improves the spectrum sensing performance, some falsely reporting malicious users may degrade the performance rigorously. In this thesis, the detection and nullifying the harmful effects of such malicious users is studied by applying some well known outlier detection methods, i.e., Grubb’s test, Boxplot method and Dixon’s test in cooperative spectrum sensing. Initially, their performance is compared from receiver operating characteristic curves (ROC) and found that Boxplot method performs better among them. However, the limitation of Dixon’s test is also discussed. 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 fusion center to decide whether the spectrum is idle for the improvement in the reliability of cooperative spectrum sensing in cognitive radio networks.