The increase in attacks on Internet of Things (IoT) systems in recent years is a concern, especially considering their growing application in industrial environments (IIoT) and the projected value they are expected to reach shortly. In light of this, the article describes the development of a proof of concept for an IIoT architecture that employs artificial intelligence techniques to detect real-time network attacks in IIoT private network environments. The approach included training and testing various supervised and unsupervised machine learning, as well as deep learning algorithms. The training process showed that real-time analysis is feasible, using SVM-based models for smaller networks and autoencoders for larger, more complex networks. In terms of performance, models trained with SVM clearly outperform others because they achieve perfect classification results with the training data, and SVMs have a maximum response time of about 5 seconds for 70,000 requests. In contrast, autoencoders respond to the same type of attack and requests always within 1 second. Additionally, the proof of concept shows that the proposed modular architecture can effectively visualize detected network attacks while satisfying requirements for real-time packet collection and analysis.
Keywords
Real Time, Network Security, Internet of Things, Network, SVM.
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Luay Abdulwahid Shihab
Department of Basic Sciences, University of Basrah, Basrah, Iraq.
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Cite this article
Luay Abdulwahid Shihab, “Real Time Attack Prevention for Industrial IoT Network”, Journal of Machine and Computing, vol.5, no.4, pp. 2688-2705, October 2025, doi: 10.53759/7669/jmc202505206.
