Published May 27, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Machine Learning-Based Detection of Wormhole Attacks in IoT Networks Using Classification Models

Authors/Creators

  • 1. Department of Cybersecurity, Taif University, Taif, Saudi Arabia

Contributors

Contact person:

  • 1. Department of Cybersecurity, Taif University, Taif, Saudi Arabia
  • 2. Assistant Professor, Department of Information Technology, Taif University, Taif, Saudi Arabia

Description

Abstract: The widespread adoption of Internet of Things (IoT) networks has introduced new cybersecurity challenges, particularly wormhole attacks. These attacks pose a significant threat to IoT environments by manipulating network routing without altering packet contents, making them difficult to detect using traditional intrusion detection systems (IDS). This study explores the application of machine learning (ML) techniques for detecting wormhole attacks in IoT networks. The research compares five machine learning classifiers: Sparse Representation Classifier (SRC), Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and XGBoost, based on metrics such as accuracy, precision, recall, F1-score, and computational efficiency. Data preprocessing techniques were applied to a publicly available IoT dataset to improve the performance of these models. Among the classifiers tested, XGBoost demonstrated superior performance with a detection accuracy of 99.97%, outpacing both traditional and deep learning models. The results highlight the potential of ensemble learning approaches in enhancing IoT security, especially for real-time applications in resource-constrained environments. The study underscores the importance of balancing detection accuracy with computational efficiency when selecting models for dynamic IoT networks. Future work will explore federated learning and hybrid deep learning models to further improve the detection capabilities of wormhole attacks in IoT settings.

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Additional details

Identifiers

EISSN
2277-3878
DOI
10.35940/ijrte.A8226.14010525

Dates

Accepted
2025-05-15
Manuscript Received on 21 March 2025 | First Revised Manuscript Received on 27 March 2025 | Second Revised Manuscript Received on 22 April 2025 | Manuscript Accepted on 15 May 2025 | Manuscript published on 30 May 2025

References

  • Thamilarasu, G., & Chawla, S. (2019). Towards Deep-Learning-Driven intrusion detection for the internet of things. Sensors, 19(9), 1977. DOI: https://doi.org/10.3390/s19091977
  • Alghamdi, R., & Bellaiche, M. (2022c). A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks. Computers & Security, 125, 103014. DOI: https://doi.org/10.1016/j.cose.2022.103014
  • Reddy, R. C. S., Mallikarjuna, A. G., Rathaiah, M., Reddy, B. S., Raghava, E. V., Srinivas, T. a. S., Sarabu, A., Ramasekhar, G., & Suneetha, S. (2024b). Wormhole detection scheme with adaptive deep neural network and hybrid Multi-Objective mitigation for the Internet of Things. African Journal of Biomedical Research. DOI: https://doi.org/10.53555/ajbr.v27i3.5445
  • Tatar, E. E., & Dener, M. (2021b). Wormhole attacks in IoT-based networks. 2021 6th International Conference on Computer Science and Engineering (UBMK), 68, 478–482. DOI: https://doi.org/10.1109/ubmk52708.2021.9558996
  • Thamilarasu, G., & Chawla, S. (2019b). Towards Deep-LearningDriven intrusion detection for the internet of things. Sensors, 19(9), 1977. DOI: https://doi.org/10.3390/s19091977
  • Alshehri, A. H. (2024b). Wormhole attack detection and mitigation model for Internet of Things and WSN using machine learning. PeerJ Computer Science, 10, e2257. DOI: https://doi.org/10.7717/peerj-cs.2257
  • Tun, Z., & Maw, A. H. (2008). Wormhole attack detection in wireless sensor networks. World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 2(10), 2184–2189. https://www.ucsy.edu.mm/ucsy/publications/wireless/WASET2008.pdf
  • Prasad, M., Tripathi, S., & Dahal, K. (2019c). Wormhole attack detection in ad hoc networks using machine learning techniques. 2022 13th International Conference on Computing, Communication, and Networking Technologies (ICCCNT). DOI: https://doi.org/10.1109/icccnt45670.2019.8944634
  • Gupta, C., Singh, L., & Tiwari, R. (2022b). Wormhole attack detection techniques in ad-hoc networks: A systematic review. Open Computer Science, 12(1), 260–288. DOI: https://doi.org/10.1515/comp-2022-0245
  • Prasad, M., Tripathi, S., & Dahal, K. (2019d). Wormhole attack detection in ad hoc network using machine learning technique. 2022 13th International Conference on Computing, Communication, and Networking Technologies (ICCCNT). DOI: https://doi.org/10.1109/icccnt45670.2019.8944634
  • Abdan, M., & Seno, S. a. H. (2022b). Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad hoc Network (MANET). Wireless Communications and Mobile Computing, 2022, 1– 12. DOI: https://doi.org/10.1155/2022/2375702
  • Abdullah, A., Albaihani, A. N. A., Osman, B., & Omar, Y. (2024b). Detecting Wormhole Attack in Environmental Monitoring System for Agriculture using Deep Learning. Journal of Advanced Research in Applied Sciences and Engineering Technology, 51(2), 153–176. DOI: https://doi.org/10.37934/araset.51.2.153176
  • Hanif, M., Ashraf, H., Jalil, Z., Jhanjhi, N. Z., Humayun, M., Saeed, S., & Almuhaideb, A. M. (2022b). AI-Based wormhole attack detection techniques in wireless sensor networks. Electronics, 11(15), 2324. DOI: https://doi.org/10.3390/electronics11152324
  • Zahra, F., Jhanjhi, N., Brohi, S. N., Khan, N. A., Masud, M., & AlZain, M. A. (2022b). Rank and wormhole attack detection model for RPLbased Internet of Things using Machine learning. Sensors, 22(18), 6765. DOI: https://doi.org/10.3390/s22186765
  • Khan, M. S., Nath, T. D., Hossain, M. M., Mukherjee, A., Hasnath, H. B., Meem, T. M., & Khan, U. (2023b). Comparison of multiclass classification techniques using the dry bean dataset. International Journal of Cognitive Computing in Engineering, 4, 6–20. DOI: https://doi.org/10.1016/j.ijcce.2023.01.002
  • Thakker, Z. L., & Buch, S. H. (2024b). Effect of feature scaling preprocessing techniques on machine learning algorithms to predict particulate matter concentration for Gandhinagar, Gujarat, India. International Journal of Scientific Research in Science and Technology, 410–419. DOI: https://doi.org/10.32628/ijsrst52411150
  • Hajigholam, M., Raie, A., & Faez, K. (2020b). Using sparse representation Classifier (SRC) to calculate dynamic coefficients for multitask joint spatial pyramid matching. Iranian Journal of Science and Technology Transactions of Electrical Engineering, 45(1), 295–307. DOI: https://doi.org/10.1007/s40998-020-00351-3
  • Elghamrawy, S. M., Lotfy, M. O., & Elawady, Y. H. (2022b). An intrusion detection model based on deep learning and multi-layer perceptron in the internet of things (IoT) network. In Lecture notes on data engineering and communications technologies (pp. 34–46). DOI: https://doi.org/10.1007/978-3-031-03918-8_4
  • Gatea, M. J., & Hameed, S. M. (2022b). An internet of things botnet detection model using regression analysis and linear discrimination analysis. Iraqi Journal of Science, 4534–4546. DOI: https://doi.org/10.24996/ijs.2022.63.10.36
  • Hasan, M. K., Ghazal, T. M., Alkhalifah, A., Bakar, K. a. A., Omidvar, A., Nafi, N. S., & Agbinya, J. I. (2021b). Fischer Linear Discrimination and Quadratic Discrimination Analysis–Based Data Mining Technique for Internet of Things Framework for Healthcare. Frontiers in Public Health, 9. DOI: https://doi.org/10.3389/fpubh.2021.737149
  • Wang, X., & Lu, X. (2020b). A Host-Based anomaly detection framework using XGBoost and LSTM for IoT devices. Wireless Communications and Mobile Computing, 2020, 1–13. DOI: https://doi.org/10.1155/2020/8838571
  • Ntayagabiri, J. P., Bentaleb, Y., Ndikumagenge, J., & Makhtoum, H. E. (2025). A comparative analysis of supervised Machine learning algorithms for IoT attack detection and Classification. Journal of Computing Theories and Applications, 2(3), 395–409. DOI: https://doi.org/10.62411/jcta.11901
  • Selladevi, M., Lathamaheswari, T., & Duraisamy, S. (2019). A Hybridized Immune System for Avoidance of Wormhole Attacks in Manet. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 2189–2195). DOI: https://doi.org/10.35940/ijeat.a9707.109119
  • Thapar, S., & Sharma, S. K. (2020). Wormhole Attack Isolation Access from Mobile Ad hoc Network with Delay Prediction Method. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 6, pp. 3672–3680). DOI: https://doi.org/10.35940/ijrte.f8230.038620
  • Murty, M. V. D. S. K., & Rajamani, Dr. L. (2023). Neighbour Node Ratio AODV (NNR-AODV) Routing Protocol for Wormhole Attack Detection in MANETs. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 4, pp. 1–9). DOI: https://doi.org/10.35940/ijese.d2547.0311423
  • T. J. Nagalakshmi, P. C. Kishore Raja, S. Pravin Kumar, V. Veeramanikandan, Intrusion Detection System using One Class SVM with and without Feature Selection in Wormhole Attack Detection. (2019). In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 2S4, pp. 629–638). DOI: https://doi.org/10.35940/ijitee.b1230.1292s419