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      Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information

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          Abstract

          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.

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          Most cited references 44

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          Multiple emitter location and signal parameter estimation

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            The Sybil Attack

             John Douceur (2002)
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              Secure routing in wireless sensor networks: attacks and countermeasures

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                15 March 2018
                March 2018
                : 18
                : 3
                Affiliations
                [1 ]Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China; michael3769@ 123456163.com (C.W.); kurtcobian4ever@ 123456163.com (L.Z.); deviltangv@ 123456163.com (Z.Z.); guashushang89757@ 123456163.com (L.Y.)
                [2 ]Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China
                [3 ]College of Computer and Control Engineering, Nankai University, Tianjin 300350, China; liuzheli@ 123456nankai.edu.cn
                [4 ]Department of Computer Science, Middlesex University, London NW4 4BT, UK; X.Cheng@ 123456mdx.ac.uk
                Author notes
                [* ]Correspondence: gongliangyi@ 123456tjut.edu.cn ; Tel.: +86-158-2228-4607
                Article
                sensors-18-00878
                10.3390/s18030878
                5877424
                29543773
                © 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 ( http://creativecommons.org/licenses/by/4.0/).

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