Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
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 p...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/3/878 |
| _version_ | 1811303442806734848 |
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| author | Chundong Wang Likun Zhu Liangyi Gong Zhentang Zhao Lei Yang Zheli Liu Xiaochun Cheng |
| author_facet | Chundong Wang Likun Zhu Liangyi Gong Zhentang Zhao Lei Yang Zheli Liu Xiaochun Cheng |
| author_sort | Chundong Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T07:46:55Z |
| format | Article |
| id | doaj.art-77fad59dcd634d3a9bbf36a6ceb3eaf8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:46:55Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-77fad59dcd634d3a9bbf36a6ceb3eaf82022-12-22T02:55:39ZengMDPI AGSensors1424-82202018-03-0118387810.3390/s18030878s18030878Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel InformationChundong Wang0Likun Zhu1Liangyi Gong2Zhentang Zhao3Lei Yang4Zheli Liu5Xiaochun Cheng6Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, ChinaKey Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, ChinaKey Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, ChinaKey Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, ChinaKey Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, ChinaCollege of Computer and Control Engineering, Nankai University, Tianjin 300350, ChinaDepartment of Computer Science, Middlesex University, London NW4 4BT, UKWith 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.http://www.mdpi.com/1424-8220/18/3/878channel state informationSybil attackindoor AoA technologyDBSCAN algorithm |
| spellingShingle | Chundong Wang Likun Zhu Liangyi Gong Zhentang Zhao Lei Yang Zheli Liu Xiaochun Cheng Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information Sensors channel state information Sybil attack indoor AoA technology DBSCAN algorithm |
| title | Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information |
| title_full | Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information |
| title_fullStr | Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information |
| title_full_unstemmed | Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information |
| title_short | Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information |
| title_sort | accurate sybil attack detection based on fine grained physical channel information |
| topic | channel state information Sybil attack indoor AoA technology DBSCAN algorithm |
| url | http://www.mdpi.com/1424-8220/18/3/878 |
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