A similarity algorithm based on hamming distance used to detect malicious users in cooperative spectrum sensing
by Libin Xu; Pin Wan; Yonghua Wang; Ting Liang
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 14, No. 1, 2019

Abstract: The collaborative spectrum sensing (CSS) methods have been proposed to improve the sensing performance. However, studies rarely take security into account. CSS methods are vulnerable to the potential attacks from malicious users (MUs). Most existing MU detection methods are reputation-based, it is incapable as the attack model is intelligent. In this paper, a Hamming distance check (HDC) is proposed to detect MUs. The Hamming distance between all the sensing nodes is calculated. Because the reports from MUs are different from honest users (HUs), we can find the MUs and exclude them from the fusion process. A new trust factor (TF) is proposed to increase the effects of trustworthy nodes in the final decision. The proposed algorithm can effectively detect the MUs without prior knowledge. In addition, our proposed method can perform better than the existing approaches.

Online publication date: Tue, 21-May-2019

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