Accurate Sybil attack detection based on fine-grained physical channel information

Wang, Chundong, Zhu, Likun, Gong, Liangyi, Zhao, Zhentang, Yang, Lei, Liu, Zheli and Cheng, Xiaochun ORCID logoORCID: (2018) Accurate Sybil attack detection based on fine-grained physical channel information. Sensors, 18 (3) . ISSN 1424-8220 [Article] (doi:10.3390/s18030878)

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With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks

Item Type: Article
Additional Information: Article number = 878
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: 24577
Notes on copyright: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
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Depositing User: Xiaochun Cheng
Date Deposited: 09 Jul 2018 18:39
Last Modified: 29 Nov 2022 20:06

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