Channel state information-based detection of Sybil attacks in wireless networks

Wang, Chundong, Zhu, Likun, Gong, Liangyi, Zhao, Zhentang, Yang, Lei, Liu, Zheli and Cheng, Xiaochun ORCID: https://orcid.org/0000-0003-0371-9646 (2018) Channel state information-based detection of Sybil attacks in wireless networks. Journal of Internet Services and Information Security, 8 (1) . pp. 2-17. ISSN 2182-2069 (doi:10.22667/JISIS.2018.02.28.002)

Abstract

Single authentication mechanisms and broadcast characteristics of wireless networks make the Access Point (AP) vulnerable to spoofing attacks and Sybil attacks. However, Sybil attacks seriously affect network performance. Sybil nodes act with different identity, and prevent the normal clients from transmission. In this paper, a self-adaptive MUSIC algorithm is proposed, which improves the accuracy of the angle of the indoor wireless device by eliminating the phase offset in channel state information (CSI), and designs different types’ detection algorithm of Sybil attacks and spoofing attacks based on different Sybil attack models. And we experiment on mobile and commercial WiFi devices. The average detection error of angle is below 6.3°. After combining analysis of received signal strength indicator (RSSI), our detection algorithm can effectively detect whether the nodes launched by Sybil attacks, and the identity of other clients disguised by spoofing attacks. According to the experimental results, the scheme can distinguish the Sybil clients and the normal clients accurately, and the average success rate of the Sybil attack detection system is 98.5%.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Artificial Intelligence group
A. > School of Science and Technology > Computer and Communications Engineering
Item ID: 24576
Useful Links:
Depositing User: Xiaochun Cheng
Date Deposited: 09 Jul 2018 18:51
Last Modified: 30 Oct 2019 21:04
URI: https://eprints.mdx.ac.uk/id/eprint/24576

Actions (login required)

Edit Item Edit Item