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AUTHOR(S): Amjad Jumaah Frhan
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ABSTRACT New security issues, such as how to protect networks and edge devices, have arisen with the introduction of the Industrial Internet of Things (IIoT). The ever-changing nature and complexity of IIoT communications makes traditional intrusion detection systems (IDS) inadequate. Using the Edge-IIoTset dataset, we present an IDS hybrid model in this study. Improving detection performance and reducing false alarms are achieved through the employment of ML-DL algorithms. When compared to standalone methods, experimental results demonstrate that the hybrid approach can provide excellent levels of accuracy, recall, and precision. The model's performance and validation in real IIoT contexts are illustrated by figures and statistics tables. |
KEYWORDS Artificial Neural Networks, Cybersecurity, Internet of Things, Machine Learning, SVM and XGBoost |
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Cite this paper Amjad Jumaah Frhan. (2026) Hybrid Intrusion Detection with Edge-IIoTset Dataset. International Journal of Education and Learning Systems, 11, 39-45 |
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