A Systematic Analysis on the Use of AI Techniques in Industrial IoT DDoS Attacks Detection, Mitigation and Prevention

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 IIoT 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, often integrated with traditional anomaly detection, signature-based systems, and blockchain technology. These hybrid approaches enhance real-time threat identification, adaptive defence mechanisms, and decentralized trust management, addressing the evolving sophistication of DDoS attacks. The study highlights AI’s potential to strengthen IIoT security and resilience, particularly in Critical National Infrastructures (CNIs), 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 IIoT’s unique constraints. The paper identifies current research challenges and outlines future directions, emphasizing the integration of AI with emerging technologies like edge computing and federated learning to advance proactive, scalable DDoS defence mechanisms in industrial ecosystems