Wireless sensor networks are vulnerable to Sybil attacks because low-cost sensor nodes have restricted energy, computation and communication resources, while a malicious device may claim multiple identities and distort routing, localization and trust decisions. Existing Sybil attack detectors based only on identity verification or received signal strength indicator may be sensitive to radio-noise fluctuations and may ignore the energy and timing behaviour of interacting nodes. This paper develops a nonlinear graphbased received-signal-strength-indicator–energy anomaly model for Sybil attack detection in wireless sensor networks and combines it with support vector machine classification. The network is represented as a time-dependent weighted graph in which node interaction weights depend on received-signal-strength-indicator deviation, residual-energy variation, transmission-time inconsistency and duplicate identity or location evidence. A nonlinear anomaly map transforms the hybrid score into a bounded risk indicator, and the support vectormachine classifier is formulated as an optimization problemover the proposed feature space. Boundedness, monotonicity, threshold-separation and computational-complexity properties of the detector are established. Simulation experiments using networks of different sizes are used to evaluate the model under legitimate and Sybil-node populations, including ablation tests, threshold-sensitivity analysis and confusion-matrix based metrics. The results indicate that the proposed framework provides an interpretable and lightweight mathematical mechanism for malicious-node detection in resource-constrained wireless sensor networks, especially when explainability, low computational overhead and crosslayer feature integration are preferred over black-box detection alone.
- Open Access
- Article
A Nonlinear RSSI–Energy Graph Model with SVM-Based Optimization and Simulation Validation for Sybil Attack Detection in Wireless Sensor Networks
Author Information
Received: 22 May 2026 | Revised: 26 Jun 2026 | Accepted: 07 Aug 2026 | Published: 01 Sep 2026
Abstract
Keywords
Sybil attack | wireless sensor network | nonlinear graph model | received signal strength indicator | energy-aware anomaly detection | support vector machine
References
- 1.
Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y.; et al. Wireless Sensor Networks: A Survey. Comput. Netw. 2002, 38, 393–422.
- 2.
Yick, J.; Mukherjee, B.; Ghosal, D. Wireless Sensor Network Survey. Comput. Netw. 2008, 52, 2292–2330.
- 3.
Douceur, J.R. The Sybil Attack. In Peer-to-Peer Systems, Proceedings of the 1st International Workshop on Peer-to-Peer Systems, Cambridge, MA, USA, 7–8 March 2002; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2002; Volume 2429, pp. 251–260.
- 4.
Newsome, J.; Shi, E.; Song, D.; et al. The Sybil Attack in Sensor Networks: Analysis and Defenses. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks, Berkeley, CA, USA, 26–27 April 2004; pp. 259–268.
- 5.
Zhang, K.; Liang, X.; Lu, R.; et al. Sybil Attacks and Their Defenses in the Internet of Things. IEEE Internet Things J. 2014, 1, 372–383.
- 6.
Demirbas, M.; Song, Y. An RSSI-Based Scheme for Sybil Attack Detection in Wireless Sensor Networks. In Proceedings of the 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks, Buffalo-Niagara Falls, NY, USA, 26–29 June 2006; pp. 564–570.
- 7.
Cheikhrouhou, O.; Bhatti, G.M.; Alroobaea, R. A Hybrid DV-Hop Algorithm Using RSSI for Localization in Large-Scale Wireless Sensor Networks. Sensors 2018, 18, 1469.
- 8.
Bao, F.; Chen, I.R.; Chang, M.; et al. Hierarchical Trust Management for Wireless Sensor Networks and Its Applications to Trust-Based Routing and Intrusion Detection. IEEE Trans. Netw. Serv. Manag. 2012, 9, 169–183.
- 9.
Alsaedi, N.; Hashim, F.; Sali, A. Energy Trust System for Detecting Sybil Attack in Clustered Wireless Sensor Networks. In Proceedings of the IEEE 12th Malaysia International Conference on Communications, Kuching, Malaysia, 23–25 November 2015; pp. 91–95.
- 10.
Hussain, F.; Hussain, R.; Hassan, S.A.; et al. Machine Learning in IoT Security: Current Solutions and Future Challenges. IEEE Commun. Surv. Tutor. 2020, 22, 1686–1721.
- 11.
Sarker, I.H.; Abushark, Y.B.; Alsolami, F.; et al. IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model. Symmetry 2020, 12, 754.
- 12.
Apruzzese, G.; Colajanni, M.; Ferretti, L.; et al. On the Effectiveness of Machine and Deep Learning for Cyber Security. In Proceedings of the 10th International Conference on Cyber Conflict, Tallinn, Estonia, 29 May–1 June 2018; pp. 371–390.
- 13.
Alqahtani, H.; Sarker, I.H.; Kalim, A.; et al. Cyber Intrusion Detection Using Machine Learning Classification Techniques. In Proceedings of the International Conference on Computing Science, Communication and Security, Gujarat, India, 26–27 March 2020; pp. 121–131.
- 14.
Dasgupta, P.; Collins, J. A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks. AI Mag. 2019, 40, 31–43.
- 15.
Gupta, B.B.; Sheng, Q.Z., Eds. Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices; CRC Press: Boca Raton, FL, USA, 2019.
- 16.
Noor, U.; Anwar, Z.; Amjad, T.; et al. A Machine Learning-Based FinTech Cyber Threat Attribution Framework Using High-Level Indicators of Compromise. Future Gener. Comput. Syst. 2019, 96, 227–242.
- 17.
Vapnik, V.N. The Nature of Statistical Learning Theory; Springer: New York, NY, USA, 1995.
- 18.
Cortes, C.; Vapnik, V. Support-Vector Networks. Mach. Learn. 1995, 20, 273–297.
- 19.
Yan, J.; Jiang, T.; Lin, L.; et al. A Novel Sybil Attack Detection Scheme in Mobile IoT Based on Collaborate Edge Computing. EURASIP J. Wirel. Commun. Netw. 2023, 2023, 25.
- 20.
Sadeghizadeh, M. A Lightweight Intrusion Detection System Based on RSSI for Sybil Attack Detection in Wireless Sensor Networks. Int. J. Nonlinear Anal. Appl. 2022, 13, 305–320.
- 21.
Venugopal, S.; Edwin Auxilia, M.A.; Dhanusgodi, V. Lightweight Sybil Attack Detection Framework for Wireless Sensor Network with Cluster Topology. AIP Conf. Proc. 2025, 3159, 040006.
- 22.
Wu, L.; Dawod, A.Y.; Miao, F. Research on Sybil Attack Detection Method for Industrial Wireless Sensor Networks Based on CNN-BiLSTM-Attention and K-Means Clustering. Int. J. Innov. Res. Sci. Stud. 2025, 8, 1920–1929.
- 23.
Baniata, L.H.; ALDabbas, A.; Atwan, J.M.; et al. A Dual-Attention CNN–GCN–BiLSTM Framework for Intelligent Intrusion Detection in Wireless Sensor Networks. Future Internet 2026, 18, 5.
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