AI-DRIVEN ANOMALY DETECTION FOR SECURING CRITICAL INFRASTRUCTURE

Abstract

Critical infrastructure, including electrical systems and communication networks, faces increasing risks from cyber-attacks and system failures, making reliable anomaly detection essential for operational security and resilience. Traditional rule-based monitoring methods often fail to capture the complexity and evolving nature of modern threats. This study explores the application of artificial intelligence (AI) and machine learning techniques for anomaly detection to safeguard critical infrastructure. By leveraging deep learning models such as recurrent neural networks (RNNs) and Transformers, the proposed approach captures temporal and contextual dependencies in system data, enabling early detection of irregular patterns. In addition, unsupervised and self-supervised learning methods are employed to address challenges related to scarce labeled data, while reinforcement learning supports adaptive threat response strategies. Experimental evaluations on benchmark datasets demonstrate that AI-driven models significantly outperform conventional methods in terms of detection accuracy, precision, recall, and response time. The findings underscore the potential of AI to provide proactive, scalable, and adaptive defense mechanisms, thereby enhancing the reliability, availability, and security of critical infrastructure systems. Future research directions include improving model interpretability, reducing computational overhead, and enabling real-time deployment in large-scale, heterogeneous environments.