A similarity algorithm based on hamming distance used to detect malicious users in cooperative spectrum sensing Online publication date: Tue, 21-May-2019
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Computing and Networking (IJHPCN):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook