Machine Learning-Based Detection of Wormhole Attacks in IoT Networks Using Classification Models
Manar Almalki1, Samah Alajmani2
1Manar Mishal Almalki, Department of Cybersecurity, Taif University, (Taif), Saudi Arabia.
2Samah Hazzaa Alajmani, Assistance Professor, Department of Information Technology, Taif University, (Taif), Saudi Arabia.
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 | PP: 31-40 | Volume-14 Issue-1, May 2025 | Retrieval Number: 100.1/ijrte.A822614010525 | DOI: 10.35940/ijrte.A8226.14010525
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The widespread adoption of Internet of Things (IoT) networks has introduced new cybersecurity challenges, particularly in the form of 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.
Keywords: Anomaly Detection, Cybersecurity, Intrusion Detection, IoT Security, Machine Learning, Wormhole Attacks, XGBoost.
Scope of the Article: Internet of Things (IoT)