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

Wang, Chundong; Zhu, Likun; Gong, Liangyi; Zhao, Zhentang; Yang, Lei; Liu, Zheli; Cheng, Xiaochun

doi:10.3390/s18030878

Open AccessArticle

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

by

Chundong Wang

^1,2,

Likun Zhu

^1,2

,

Liangyi Gong

^1,2,*

,

Zhentang Zhao

^1,2,

Lei Yang

^1,2,

Zheli Liu

³ and

Xiaochun Cheng

⁴

¹

Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China

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

⁴

Department of Computer Science, Middlesex University, London NW4 4BT, UK

^*

Author to whom correspondence should be addressed.

Sensors 2018, 18(3), 878; https://doi.org/10.3390/s18030878

Submission received: 31 January 2018 / Revised: 4 March 2018 / Accepted: 13 March 2018 / Published: 15 March 2018

(This article belongs to the Special Issue Security, Trust and Privacy for Sensor Networks)

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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.

Keywords: channel state information; Sybil attack; indoor AoA technology; DBSCAN algorithm

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MDPI and ACS Style

Wang, C.; Zhu, L.; Gong, L.; Zhao, Z.; Yang, L.; Liu, Z.; Cheng, X. Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information. Sensors 2018, 18, 878. https://doi.org/10.3390/s18030878

AMA Style

Wang C, Zhu L, Gong L, Zhao Z, Yang L, Liu Z, Cheng X. Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information. Sensors. 2018; 18(3):878. https://doi.org/10.3390/s18030878

Chicago/Turabian Style

Wang, Chundong, Likun Zhu, Liangyi Gong, Zhentang Zhao, Lei Yang, Zheli Liu, and Xiaochun Cheng. 2018. "Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information" Sensors 18, no. 3: 878. https://doi.org/10.3390/s18030878

APA Style

Wang, C., Zhu, L., Gong, L., Zhao, Z., Yang, L., Liu, Z., & Cheng, X. (2018). Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information. Sensors, 18(3), 878. https://doi.org/10.3390/s18030878

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

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