Enhancing Resilience in Critical Infrastructure Through Deep Learning: Strategies for Risk Assessment and Mitigation

Abstract

The resilience of critical infrastructure, encompassing systems such as transportation networks, power grids, water supplies, and communication systems, is essential for societal stability and economic continuity. Traditional risk assessment and mitigation approaches often struggle to keep pace with the growing complexity and interdependencies of these systems. Deep learning offers transformative potential for enhancing resilience through advanced risk assessment and mitigation strategies. This paper explores the application of deep learning techniques to assess risks and mitigate threats in critical infrastructure systems. We analyze deep learning architectures including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), and their roles in predicting failures, detecting anomalies, and optimizing responses. We also address challenges such as data quality, model interpretability, and real-time processing. By leveraging deep learning, critical infrastructure systems can achieve improved resilience, ensuring their continued operation and recovery from disruptions.