ecuring Critical Infrastructure with World Models that Learn from Real-World Sensor and Activity Logs

Abstract: Critical infrastructure systems—such as power grids, water treatment facilities, and transportation networks—are increasingly reliant on interconnected digital technologies. This interdependence exposes them to a myriad of cyber and physical threats. Traditional security measures often fall short in addressing the dynamic and complex nature of these threats. This paper explores the development and application of AI-driven world models that learn from real-world sensor and activity logs to enhance the security of critical infrastructure. By integrating data from various sources and employing advanced machine learning techniques, these models can detect anomalies, predict potential threats, and provide actionable insights for proactive defense mechanisms. The study delves into the architecture of such models, their implementation challenges, and the potential they hold in fortifying critical infrastructure against evolving threats. Keywords: Artificial Intelligence (AI), World Models, Critical Infrastructure Protection, Real-World Sensor Data, Activity Logs, Cybersecurity, Anomaly Detection, Machine Learning, Predictive Analytics, Threat Detection, Intrusion Detection Systems (IDS), Supervisory Control and Data Acquisition (SCADA) Systems, Internet of Things (IoT), Cyber-Physical Systems, Real-Time Monitoring, Data Fusion, Deep Learning, Security Information and Event Management (SIEM), Infrastructure Resilience, Adaptive Security