AI-Powered Intrusion Detection and Response in Industrial IoT
Vishal Kumar Jain, Archan Mitra, Sanchita Paul · Advances in computational intelligence and robotics book series · 2025
The rapid adoption of the Industrial Internet of Things (IIoT) has transformed manufacturing through enhanced automation and productivity, but it has also broadened the cybersecurity attack surface. As threats grow more complex, traditional security solutions struggle to detect and mitigate intrusions in real time. This paper proposes an AI-powered intrusion detection and response framework specifically designed for smart manufacturing. Using machine learning and deep learning to analyze network traffic and device behavior, the system identifies anomalies linked to cyber threats. Edge computing and federated learning enable low-latency processing and privacy-preserving collaboration. A real-time adaptive response module dynamically isolates threats and updates defenses. Evaluated on a simulated smart factory testbed, the framework shows notable improvements in detection accuracy, speed, and reliability over conventional IDS approaches, supporting the development of resilient IIoT ecosystems.