Machine Learning for Cybersecurity in Industrial Control Systems (ICS)

Abstract

ICS (Industrial Control Systems) are the backbone of power, water, and manufacturing, amongst other critical infrastructure sectors. Like everything else, traditional ICS is evolving with the modern Information and Communication Technologies (ICT) getting integrated into its stack, exposing itself to potential cyber-attacks. Because of the real-time operational requirements and legacy technology, traditional security methods are frequently ineffective in protecting ICS. Machine Learning (ML) techniques are the major solution to improve Intrusion Detection Systems (IDS) or Intrusion Prevention Systems (IPS). This article is a survey paper in which we discussed the use of ML for cybersecurity, such as anomaly detection, integrations with crypto, and adversarial attacks within ICS. Through a discussion of the challenges and future directions around the deployment of ML models inside the ICS environment, this work aims to demonstrate how advanced technologies can help safeguard critical infrastructure against adaptive threats. More focus is given to enhancing the robustness and scalability of the ML models across device/ICS networks.

Keywords: Machine Learning, Cybersecurity, Industrial Control Systems (ICS), Intrusion Detection Systems (IDS), Anomaly Detection, Adversarial Attacks