A lightweight intrusion detection system based on RSSI for sybil attack detection in wireless sensor networks

Document Type : Research Paper


Department of Computer Engineering, Quchan University of Technology, Quchan, Iran


As the prevalence of Wireless Sensor Networks (WSNs) grows in the many mission-critical applications such as military and civil domains, the need for network security has become a critical concern. The inherently vulnerable characteristics of WSNs appoint them susceptible to various types of attacks. A particularly harmful attack against sensor and ad hoc networks is known as the Sybil attack, where a node illegitimately claims multiple identities. Sybil attacks can severely deteriorate the network performance and compromise the security by disrupting many networking protocols. This paper presents a lightweight Intrusion detection system (IDS) based on received signal strength indicator (RSSI) readings of messages to protect WSNs against Sybil attack. Our idea in the proposed method is based on the local calculation (within each node and without the need for communications) the RSSI ratio from the suspected nodes to the Sybil attack. The obtained results demonstrate that Proposed System achieves high detection accuracy, low false alarm rate and low energy consumption appointing it a promising IDS candidate for wireless sensor networks.


[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, A survey on sensor networks, in IEEE Communications Magazine, 40 (8) (2020) 102-114.
[2] R. Amuthavalli and R. S. Bhuvaneswaran, Detection and prevention of sybil attack in wireless sensor network employing random password comparison method, Journal of Theoretical and Applied Information Technologygy, 67 (1)(2013) 236–246.
[3] A. Andalib, M. Jamshidi, F. Andalib and D. Momeni, A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor Networks using Sink Nodes, International Journal of Computer Applications Technology and Research, 5 (7) (2016) 433-438.
[4] M. G. Ball, B. Qela and S. Wesolkowski, A Review of the Use of Computational Intelligence in the Design of Military Surveillance Networks, in Recent Advances in Computational Intelligence in Defense and Security, 621 (2015) 663-693.
[5] K. Butler, S. Ryu, P. Traynor and P. D. McDaniel, Leveraging Identity-Based Cryptography for Node ID Assignment in Structured P2P Systems, IEEE transaction on parallel and distributed systems, 20 (12) (2009) 1803-1815.
[6] S. Chen, G. Yang and S. Chen, A Security Routing Mechanism against Sybil Attack for Wireless Sensor Networks, in: Proc. of the International Conference on Communications and Mobile Computing, China, (2010) 142-146.
[7] M. Demirbas and Y. Song, An RSSI-based scheme for Sybil attack detection in wireless sensor networks, In: Proc. of the IEEE Computer Society International Symposium on World of Wireless, Mobile and Multimedia Networks, (2006) 570–574.
[8] A. Ghosal and S. Halder, A survey on energy efficient intrusion detection in wireless sensor networks, in Journal of Ambient Intelligence and Smart Environments, 9 (2) (2017) 239-261.
[9] D. He, N. Kumar, J. Chen, C. C. Lee and N. Chilamkurti, Robust anonymous authentication protocol for healthcare applications using wireless medical sensor networks, in Multimedia Systems, 21 (1) (2015) 49–60.
[10] A. Jangra and S. Priyanka, Securing LEACH Protocol from Sybil Attack using Jakes Channel Scheme (JCS), in: Proc. of the International Conferences on Advances in ICT for Emerging Regions, (2011).
[11] A. Jiang and L. Zheng, An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization, Sensors, 18 (4) (2018) 1020.
[12] M. Li and H. J. Lin, Design and Implementation of Smart Home Control Systems Based on Wireless Sensor Networks and Power Line Communications, in IEEE Transactions on Industrial Electronics, 62 (7)(2015) 4430–4442.
[13] M. A. Moulavi, J. Nasiri, B. Bahmani, H. Parvar, M. Sadeghizadeh and M. Naghibzadeh, DHA-KD: Dynamic Hierarchical Agent Based Key Distribution in Group Communication, 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, (2008) 301-306.
[14] C. Pang, G. Xu, G. Shan and Y. Zhang, A new energy efficient management approach for wireless sensor networks in target tracking, in Defence Technology, 17 (3) (2021) 932-947.
[15] A. Rodr´─▒guez, C. Del-Valle-Soto and R. Vel´azquez, Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on Yellow Saddle Goatfish Algorithm, Mathematics, 8 (9) (2020) 1515.
[16] M. Sadeghizadeh and O. R. Marouzi, A Lightweight Intrusion Detection System Based on Specifications to Improve Security in Wireless Sensor Networks, in Journal of Communication Engineering, 7 (2) (2018).
[17] M. Sadeghizadeh and O. R. Marouzi, Securing Cluster-heads in Wireless Sensor Networks by a Hybrid Intrusion Detection System Based on Data Mining, in Journal of Communication Engineering, 8 (1) (2019).
[18] P. Sarigiannidis, E. Karapistoli and A. A. Economides, Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information, Elsevier, Expert Systems with Applications, 42 (21) (2015) 7560-7572.
[19] P. Sarigiannidis, E. Karapistoli and A. A. Economides, Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information, Elsevier, Expert Systems with Applications, 42 (21)(2015) 7560-7572.
[20] W. Shi, S. Liu and Z. Zhang, A Lightweight Detection Mechanism against Sybil Attack in Wireless Sensor Network, KSII Transactions of Internet ad Information Systems 9 (9) (2015) 3738-3750.
[21] K. F. Ssu, W. T. Wang and W. C. Chang, Detecting Sybil attacks in wireless Sensor Networks using neighboring information, in: Proc. of the Computer Networks 53 (2009) 3042–3056.
[22] U. Suriya and R. Vayanaperumal, Detecting and Preventing Sybil Attacks in Wireless Sensor Networks Using Message Authentication and Passing Method, The Scientific World Journal, 2015(2015) 1-7.
[23] C. Wang, L. Zhu, L. Gong, Z. Zhao, L. Yang, Z. Liu and X. Cheng, Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information, Sensors, 18 (3)(2018) 878.
[24] M. Wen, H. Li, Y.F. Zheng and K.F. Chen, TDOA-Based Sybil Attack Detection Scheme for Wireless Sensor Networks, Journal of Shanghai University (English Edition), 12 (1)(2008) 66-70.
[25] K. K. Waraich and B. Singh, Performance Analysis of AODV Routing Protocol with and without Malicious Attack in Mobile Adhoc Networks, in International Journal of Advanced Science and Technology, 82 (2015) 63-70.
[26] S. Zhong, et Y. G. Liu and Y.R. Yong, Privacy-preserving location based services for mobile users in Wireless Networks, In: Proc. of the Technical Report, Yale Computer Science, (2004).
Volume 13, Issue 1
March 2022
Pages 305-320
  • Receive Date: 14 September 2020
  • Revise Date: 10 July 2021
  • Accept Date: 07 September 2021