Machine Learning for Cybersecurity in the Utilities and Power Sector: A Practical Approach to Infrastructure Protection

Abstract

Cyberattacks on critical infrastructure systems, including power grids and water treatment facilities, pose a growing threat to public safety and national security. The increasing complexity and connectivity of these systems have introduced new vulnerabilities that traditional rule-based cybersecurity solutions struggle to address. This paper presents a practical exploration of how machine learning (ML) techniques can enhance cybersecurity within the utilities and power sector. We examine common cyber threats affecting industrial control systems, explore how ML techniques—such as supervised learning and anomaly detection—can detect and mitigate these threats, and propose a simple, interpretable ML based security framework. Through real-world incidents and hypothetical use cases, we demonstrate the practicality and effectiveness of ML in improving infrastructure resilience.