Machine Learning for Proactive Cyber Threat Detection in Critical Infrastructure

Abstract

The increasing digitization and interconnectivity of critical infrastructure—such as power grids, water treatment facilities, transportation networks, and healthcare systems—have introduced significant vulnerabilities to cyber threats. Traditional rule-based security mechanisms often fail to detect advanced and stealthy cyberattacks that target these high-value systems. This paper presents a proactive threat detection framework that leverages machine learning techniques to identify anomalies and predict potential cyber incidents in real-time. By integrating supervised and unsupervised learning models—including Random Forest, Support Vector Machines (SVM), and Autoencoders—the framework continuously monitors data streams from industrial control systems (ICS), SCADA networks, and IoT-enabled sensors to detect patterns indicative of cyber intrusions. The study also explores feature engineering techniques to extract domain-specific indicators from raw telemetry data and applies ensemble learning to improve detection accuracy. Experimental validation using real-world datasets such as ICS-CERT and SWaT demonstrates that the proposed approach achieves high precision and recall while significantly reducing false positive rates. This research highlights the role of machine learning in transforming cybersecurity from a reactive defense to a proactive and predictive capability, offering a resilient safeguard for critical infrastructure systems.