A similarity algorithm based on hamming distance used to detect malicious users in cooperative spectrum sensing. (8th May 2019)

• Record Type: Journal Article
• Authors: Xu, Libin; Wan, Pin; Wang, Yonghua; Liang, Ting
• Is Part Of: International journal of high performance computing and networking. Volume 14:Number 1(2019)
• Page Start: 112
• Page End: 119
• Publication Date: 2019-05-08
• Subjects: malicious user -- attack -- Hamming distance -- cognitive radio networks; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

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.