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Malicious Users Detection and Nullifying their Effects on Cooperative Spectrum Sensing
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 Title & Authors
Malicious Users Detection and Nullifying their Effects on Cooperative Spectrum Sensing
Prasain, Prakash; Choi, Dong-You;
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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 article, we have studied the detection and nullifying the harmful effects of such malicious users by applying some well known outlier detection methods based 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 where 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 fusion center to decide whether the spectrum is idle.
Cognitve Radio;Energy Detection;Cooperative Spectrum Sensing;
 Cited by
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