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Intrusion Detection Systems from IT to IIoT: Survey and Taxonomy
1 Center for Cybersecurity, FTSM, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
2 Center for Software Technology and Management, FTSM, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
3 Center for Artificial Intelligence and Technology, FTSM, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
* Corresponding Authors: Ali Lamjid. Email: ; Khairul Akram Zainol Ariffin. Email:
Journal of Cyber Security 2026, 8, 211-240. https://doi.org/10.32604/jcs.2026.077850
Received 18 December 2025; Accepted 06 March 2026; Issue published 25 May 2026
Abstract
The convergence of Operational Technology (OT) and Information Technology (IT) within Critical Infrastructures gives rise to complex and heterogeneous network architectures in the Industrial Internet of Things (IIoT). Traditional Intrusion Detection Systems (IDS), designed for conventional IT environments, are suited for mitigating vulnerabilities inherent in these systems; however, they often fail to address vulnerabilities intrinsic to heterogeneous IIoT architectures, most notably adversarial threats. To address this challenge, this study undertakes a systematic review of 23 representative papers published between 2016 and 2025, analyzing the IIoT-based IDS approaches. Distinguishing itself from existing reviews, this work classifies IDS approaches based on deployment architecture, detection methodology, and security threat types, thereby identifying a critical gap in current defensive capabilities. This analytical framework reveals a critical deficiency in current defense mechanisms against sophisticated threats such as adversarial attacks. The proposed taxonomy provides a foundational framework for the rational design of robust hybrid IDS solutions that can secure both legacy supervisory control and data acquisition (SCADA) systems and modern smart devices. Ultimately, these findings provide a strategic road-map for researchers and practitioners to advance Cybersecurity resilience in the rapidly maturing IIoT platforms.Keywords
Cite This Article
Copyright © 2026 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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