HYBRID DEEP LEARNING APPROACHES FOR CYBERATTACK DETECTION AND PREVENTION IN CRITICAL INFRASTRUCTURE SYSTEMS

This paper proposes a new hybrid deep learning framework to predict, detect, and stop cyber-attacks in critical infrastructure systems. The goal is to increase the resilience of systems like power grids, transportation networks, and water supply systems by combining supervised learning (CNNs), unsupervised anomaly detection (Autoencoders, GANs), and reinforcement learning (DQN). Distinguishing the concentration of CT images through extensive experiments on real patients' data indicates that the proposed model is far superior to the traditional methods in accuracy, precision, and recall by 98.4%, 97.5%, and 98.2%, respectively, and lowers the rate of false positives to 3.5%. Experiment results show the efficiency of our hybrid way of detecting these well-known and new cyber threats with a good detection time (approximately 30ms). This paper helps advance cybersecurity for critical infrastructure by providing a scalable real-time defense that can react to new threats, enhancing the overall system resilience and maintainability.