AI-Powered Intrusion Detection Systems for Industrial Control Networks

Abstract

This paper examines how AI-powered intrusion detection system (AI-IDS) can be used to protect industrial control networks (ICNs) against emerging cyber threats. The primary goal is to assess the performances, performances, and the challenges of the AI-based IDS related to detecting anomalies, zero-day attacks as well as protocol specific intrusions in the insights of heterogeneous industrial environments. The research studies were synthesized through a secondary research methodology based on peer-reviewed journals, proceedings of conferences, systemic reviews of literature to compare different machine learning and deep learning models, CNNs, RNNs, ensemble and hybrid structures. Results have shown that AI-IDS is able to enhance the level of detection accuracy, remove most of the false positives, and maximize real-time mitigation of threats, whereas the conventional signature-based techniques fail to do so. It identified challenges like heterogeneity in data, computational costs and connecting to SCADA/IoT protocols. Best practice includes hybrid anomaly-signature systems, and distributed configurations of learning and automated responses. Altogether, the paper shows that AI-IDS offers strong, flexible, and scalable cybersecurity services to the contemporary control networks of industries.