Leveraging Data Science for Real-Time Threat Intelligence and Anomaly Detection in Critical Infrastructure

Abstract

Critical infrastructure systems (power, water, transportation, healthcare, and communications) are increasingly digitized and interconnected, which improves efficiency but also expands attack surfaces. Real-time threat intelligence and anomaly detection using advanced data science techniques are essential to detect, interpret, and respond to malicious activity and failures before they cascade into large-scale outages or safety incidents. This paper presents a comprehensive, research-grade synthesis of theoretical foundations, architectures, algorithms, evaluation methodologies, and deployment considerations for real-time threat intelligence and anomaly detection applied to critical infrastructure. We integrate classical statistical approaches, machine learning (ML), deep learning (DL), streaming analytics, graph analytics, and explainable AI (XAI) to construct a practical yet rigorous blueprint for systems that require high availability, low latency, interpretability, and regulatory compliance. Case studies and example designs for power grids, industrial control systems (ICS)/SCADA, transportation networks, and healthcare cyber-physical systems (CPS) illustrate tradeoffs between detection accuracy, false alarm rates, computational cost, and explainability. We conclude with an agenda for future research, including causal anomaly detection, federated/edge analytics for privacy and latency, and formal verification of ML components.