Security management with dynamic deception patching strategies for software vulnerabilities in industrial control systems

Industrial Control Systems (ICS) are critical components in various sectors such as power generation, water and wastewater treatment, oil and gas production, and transportation networks. Traditionally designed with a focus on physical reliability and security, these systems are increasingly integrated with Information Technology (IT) systems, leading to improved efficiency and remote monitoring capabilities. However, this integration has also introduced a number of vulnerabilities common in IT environments, exposing ICS to potential cyber threats. This thesis investigates dynamic deceptive patching strategies tailored to vulnerabilities in ICS software in order to improve security management. This research aims to develop robust defense mechanisms against sophisticated cyber attacks targeting ICS by using advanced machine learning and deep learning techniques, including adversarial training and honeypot designs. The study evaluates the effectiveness of these methods using various datasets and provides a comparative analysis of traditional patching approaches. The findings show that dynamic deception strategies can significantly increase the resilience of ICS against cyber threats and provide a comprehensive security solution that addresses both IT and operational technology (OT) vulnerabilities. This thesis contributes to ensuring the continuity of reliability and security of essential services to protect critical infrastructure from cyber attacks.