Accurate Sybil attack detection based on fine-grained physical channel information
Article
Wang, C., Zhu, L., Gong, L., Zhao, Z., Yang, L., Liu, Z. and Cheng, X. 2018. Accurate Sybil attack detection based on fine-grained physical channel information. Sensors. 18 (3). https://doi.org/10.3390/s18030878
| Type | Article |
|---|---|
| Title | Accurate Sybil attack detection based on fine-grained physical channel information |
| Authors | Wang, C., Zhu, L., Gong, L., Zhao, Z., Yang, L., Liu, Z. and Cheng, X. |
| 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 |
| Research Group | Artificial Intelligence group |
| Publisher | MDPI |
| Journal | Sensors |
| ISSN | 1424-8220 |
| Publication dates | |
| Online | 15 Mar 2018 |
| Mar 2018 | |
| Publication process dates | |
| Deposited | 09 Jul 2018 |
| Accepted | 13 Mar 2018 |
| Submitted | 31 Jan 2018 |
| Output status | Published |
| Publisher's version | License |
| Copyright Statement | © 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/). |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/s18030878 |
| Web of Science identifier | WOS:000428805300199 |
| Language | English |
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Zhao, T., Yang, Y., Wang, E., Mumtaz, S. and Cheng, X. 2021. Task bundling in worker-centric mobile crowdsensing. International Journal of Intelligent Systems. 36 (9), pp. 4936-4961. https://doi.org/10.1002/int.22497ShadowFPE: new encrypted web application solution based on shadow DOM
Guo, X., Huang, Y., Ye, J., Yin, S., Li, M., Li, Z., Yiu, S. and Cheng, X. 2021. ShadowFPE: new encrypted web application solution based on shadow DOM. Mobile Networks and Applications. 26 (4), pp. 1733-1746. https://doi.org/10.1007/s11036-019-01509-yLearning context-aware outfit recommendation
Abugabah, A., Cheng, X. and Wang, J. 2020. Learning context-aware outfit recommendation. Symmetry. 12 (6), pp. 1-13. https://doi.org/10.3390/sym12060873