Smart Intrusion Detection and Prevention for IIoT Using AI

Abstract

With the growth in industrial automation, Industrial IoT (IIoT) systems become more vulnerable to cyber attacks, and intelligent Intrusion Detection and Prevention Systems (IDPS) are needed. This chapter delves into the ways in which AI improves IDPS by detecting and preventing security threats in real time. It discusses machine learning and deep learning models employed in network anomaly detection, such as decision trees, support vector machines, and neural networks. Main points of discussion are signature-based and anomaly-based detection, challenges in datasets, as well as in-field deployment. The chapter also outlines how IDPS powered by AI can adjust to emerging threats, minimize false positives, and integrate into prevailing cybersecurity architectures. Real-world applications of AI-fueled IDPS are illustrated through case studies, providing an insight into their effectiveness in securing IIoT infrastructures.