3b238e271970461e6db5704 20250526095251375 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 05 30 2025 14 1 Department of Cybersecurity, Taif University, Taif, Saudi Arabia. Manar Mishal Almalki https://orcid.org/0009-0002-0678-5083 Samah Hazzaa Alajmani https://orcid.org/0009-0000-7152-9559 Assistant Professor, Department of Information Technology, Taif University, Taif, Saudi Arabia. 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. 05 30 2025 05 30 2025 31 40 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE) Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Based on my understanding, this article has no conflicts of interest. This article has not been sponsored or funded by any organization or agency. The independence of this research is a crucial factor in affirming its impartiality, as it has been conducted without any external sway. The data provided in this article is exempt from the requirement for ethical approval or participant consent. The adequate resources of this article are publicly accessible. The authorship of this article is contributed equally to all participating individuals. 10.35940/ijrte.A8226.14010525 https://www.ijrte.org/portfolio-item/A822614010525/