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

Sensors - Tập 18 Số 3 - Trang 878
Chundong Wang1,2, Likun Zhu1,2, Lanqi Yang1,2, Zhentang Zhao1,2, Lei Yang1,2, Zheli Liu3, Xiaochun Cheng4
1Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin 300384, China
2Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China
3College of Computer and Control Engineering, Nankai University, Tianjin, 300350, China
4Department of Computer Science, Middlesex University, London NW4 4BT, UK

Tóm tắt

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.

Từ khóa


Tài liệu tham khảo

Mitchell, C. (2005, January 3–4). Security Analysis and Improvements for IEEE 802.11 i. Proceedings of the 12th Annual Network and Distributed System Security Symposium (NDSS’05), San Diego, CA, USA.

Douceur, J.R. (2002, January 7–8). The Sybil attack. Proceedings of the International Workshop on Peer-to-Peer Systems, Cambridge, MA, USA.

Zhang, 2016, Optimal DoS attack scheduling in wireless networked control system, IEEE Trans. Control Syst. Technol., 24, 843, 10.1109/TCST.2015.2462741

Demirbas, M., and Song, Y. (2006, January 26–29). An RSSI-based scheme for Sybil attack detection in wireless sensor networks. Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks, Buffalo NY, USA.

Wang, J., Yang, G., Sun, Y., and Chen, S. (2007, January 21–25). Sybil attack detection based on RSSI for wireless sensor network. Proceedings of the IEEE International Conference on Wireless Communications, Networking and Mobile Computing, WiCom 2007, Shanghai, China.

Jan, M.A., Nanda, P., He, X., and Liu, R.P. (2015, January 20–22). A Sybil Attack Detection Scheme for a Centralized Clustering-based Hierarchical Network. Proceedings of the IEEE Trustcom/BigDataSE/ISPA, Helsinki, Finland.

Garip, M.T., Kim, P.H., Reiher, P., and Gerla, M. (2017, January 8–11). INTERLOC: An interference-aware RSSI-based localization and Sybil attack detection mechanism for vehicular ad hoc networks. Proceedings of the IEEE 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA.

Deak, 2010, Filters for RSSI-based measurements in a Device-free Passive Localisation Scenario, Image Process. Commun., 15, 23

Vasisht, D., Kumar, S., and Katabi, D. (2016, January 16–18). Decimeter-Level Localization with a Single WiFi Access Point. Proceedings of the NSDI, Santa Clara, CA, USA.

Kotaru, 2015, Spotfi: Decimeter level localization using wifi, Proceedings of the ACM SIGCOMM Computer Communication Review, Volume 45, 269, 10.1145/2829988.2787487

Wang, 2018, Channel State Information-Based Detection of Sybil Attacks in Wireless Networks, J. Internet Serv. Inf. Secur., 8, 2

Sen, S., Radunovic, B., Choudhury, R.R., and Minka, T. (2012, January 25–29). You are facing the Mona Lisa: Spot localization using PHY layer information. Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, Lake District, UK.

Zhang, 2010, Through-the-wall target localization with time reversal music method, Prog. Electromagn. Res., 106, 75, 10.2528/PIER10052408

Gjengset, J., Xiong, J., McPhillips, G., and Jamieson, K. (2014, January 7–11). Phaser: Enabling phased array signal processing on commodity WiFi access points. Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, Maui, HI, USA.

Gan, J., and Tao, Y. (June, January 31). DBSCAN Revisited. Proceedings of the ACM SIGMOD International Conference, Melbourne, Australia.

Karlof, 2003, Secure routing in wireless sensor networks: Attacks and countermeasures, Ad Hoc Netw., 1, 293, 10.1016/S1570-8705(03)00008-8

Newsome, J., Shi, E., Song, D., and Perrig, A. (2004, January 26–27). The Sybil attack in sensor networks: Analysis & defences. Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks, Berkeley, CA, USA.

Zhang, 2006, Location-based compromise-tolerant security mechanisms for wireless sensor networks, IEEE J. Sel. Areas Commun., 24, 247, 10.1109/JSAC.2005.861382

Dhamodharan, U.S.R.K., and Vayanaperumal, R. (2015). Detecting and preventing Sybil attacks in wireless sensor networks using message authentication and passing method. Sci. World J., 2015.

Pecori, 2016, 3AKEP: Triple-Authenticated Key Exchange Protocol for Peer-to-Peer VoIP Applications, Comput. Commun., 85, 28, 10.1016/j.comcom.2016.04.005

Pecori, 2016, S-Kademlia: A trust and reputation method to mitigate a Sybil attack in Kademlia, Comput. Netw., 94, 205, 10.1016/j.comnet.2015.11.010

Ishida, 2017, Implementation of an Integrated Disaster Information Cloud System for Disaster Control, J. Internet Serv. Inf. Secur., 7, 1

Cui, 2016, Key-aggregate searchable encryption (KASE) for group data sharing via cloud storage, IEEE Trans. Comput., 65, 2374, 10.1109/TC.2015.2389959

Liu, 2016, New order preserving encryption model for outsourced databases in cloud environments, J. Netw. Comput. Appl., 59, 198, 10.1016/j.jnca.2014.07.001

Liu, 2018, Verifiable searchable encryption with aggregate keys for data sharing system, Future Gener. Comput. Syst., 78, 778, 10.1016/j.future.2017.02.024

Liu, Z., Huang, Y., Li, J., Cheng, X., and Shen, C. (2018). DivORAM: Towards a Practical Oblivious RAM with Variable Block Size. Information Sciences. Inf. Sci., 447.

Xu, 2016, Device fingerprinting in wireless networks: Challenges and opportunities, IEEE Commun. Surv. Tutor., 18, 94, 10.1109/COMST.2015.2476338

Park, 2013, Defense against Sybil attack in the initial deployment stage of vehicular ad hoc network based on roadside unit support, Secur. Commun. Netw., 6, 523, 10.1002/sec.679

Shen, 2016, A lightweight multi-layer authentication protocol for wireless body area networks, Future Gener. Comput. Syst., 78, 956, 10.1016/j.future.2016.11.033

Shi, W., Liu, S., and Zhang, Z. (2015). A Lightweight Detection Mechanism against Sybil Attack in Wireless Sensor Network. KSII Trans. Internet Inf. Syst., 9.

Gu, P., Khatoun, R., Begriche, Y., and Serhrouchni, A. (2017, January 11–12). k-Nearest Neighbours classification based Sybil attack detection in Vehicular networks. Proceedings of the IEEE 2017 Third International Conference on Mobile and Secure Services (MobiSecServ), Miami Beach, FL, USA.

Gu, P., Khatoun, R., Begriche, Y., and Serhrouchni, A. (2017, January 19–22). Support Vector Machine (SVM) Based Sybil Attack Detection in Vehicular Networks. Proceedings of the 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA.

Wen, 2008, TDOA-based Sybil attack detection scheme for wireless sensor networks, J. Shanghai Univ., 12, 66, 10.1007/s11741-008-0113-2

Zhang, 2010, AOA based trust evaluation scheme for Sybil attack detection in WSN, Appl. Res. Comput., 27, 1847

Yu, 2013, Detecting Sybil attacks in VANETs, J. Parallel Distrib. Comput., 73, 746, 10.1016/j.jpdc.2013.02.001

Liu, 2015, The Mason test: A defence against Sybil attacks in wireless networks without trusted authorities, IEEE Trans. Mob. Comput., 14, 2376, 10.1109/TMC.2015.2398425

Feng, X., Li, C.Y., Chen, D.X., and Tang, J. (2015). EBRS: Event Based Reputation System for Defensing Multi-Source Sybil Attacks in VANET, Springer International Publishing.

Liu, R., and Wang, Y. (2014, January 15–16). A New Sybil Attack Detection for Wireless Body Sensor Network. Proceedings of the Tenth International Conference on Computational Intelligence and Security, Kunming, China.

Xiao, 2009, Channel-based detection of Sybil attacks in wireless networks, IEEE Trans. Inf. Forensics Secur., 4, 492, 10.1109/TIFS.2009.2026454

Zhang, 2017, Energy-efficient data-gathering rendezvous algorithms with mobile sinks for wireless sensor networks, Int. J. Sens. Netw., 23, 248, 10.1504/IJSNET.2017.083533

Zhang, 2016, Efficient algorithm for k-barrier coverage based on integer linear programming, China Commun., 13, 16, 10.1109/CC.2016.7489970

Jamshidi, 2016, A lightweight algorithm for detecting mobile Sybil nodes in mobile wireless sensor networks, Comput. Electr. Eng., 64, 220, 10.1016/j.compeleceng.2016.12.011

Schmidt, 1986, Multiple emitter location and signal parameter estimation, IEEE Trans. Antennas Propag., 34, 276, 10.1109/TAP.1986.1143830

Xiong, J., and Jamieson, K. (2013). Arraytrack: A Fine-Grained Indoor Location System, USENIX.

Wang, 2017, Device-Free Human Activity Recognition Using Commercial WiFi Devices, IEEE J. Sel. Areas Commun., 35, 1118, 10.1109/JSAC.2017.2679658

Halperin, 2011, Tool release: Gathering 802.11 n traces with channel state information, ACM SIGCOMM Comput. Commun. Rev., 41, 53, 10.1145/1925861.1925870

Thông tin
Thông tin xuất bản

Sensors

Tập 18 Số 3

878

Thông tin tác giả