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Alemayehu, Mikiyas, Ghanem, Mohamed Chahine, Kheddar, Hamza, Dunsin, Dipo and Lacerda, Marcio J. (2026) Systematic analysis on the use of AI techniques in industrial IoT DDoS attack detection, mitigation, and prevention. IoT, 7(3) (51). pp. 1-56. ISSN 2624-831X
Distributed Denial of Service (DDoS) attacks pose significant threats to Industrial Internet of Things (IIoT) environments, exacerbated by the resource constraints of IoT devices and the disruptive impact of such attacks. Conventional detection and prevention methods fall short of ensuring the availability and operational continuity required in industrial deployments. This article systematically analyses artificial intelligence (AI) techniques for detecting, preventing, and mitigating DDoS attacks in IIoT systems. We examine diverse AI-driven solutions, including machine learning (ML) and deep learning (DL) models, alongside hybrid approaches that enhance real-time threat identification, adaptive defence mechanisms, and decentralised trust management, addressing the evolving sophistication of DDoS attacks. This study highlights AI’s potential to strengthen IIoT security and resilience, particularly in critical national infrastructure (CNI), where uninterrupted operations are paramount. However, challenges such as computational overhead, model interpretability, and dataset scarcity in industrial settings remain critical barriers. Additionally, the dynamic IIoT topology and heterogeneous device ecosystems necessitate context-aware AI solutions. This analysis underscores the need for lightweight, explainable AI frameworks and collaborative defence strategies tailored to the IIoT’s unique constraints. It emphasises the integration of AI with emerging technologies like edge computing and federated learning to advance proactive, scalable DDoS defence mechanisms in industrial ecosystems.
Available under License Creative Commons Attribution 4.0.
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