Survey of deep learning approaches for securing industrial control systems: A comparative analysis

Abstract

In an era where critical infrastructure (CI) underpins our daily lives spanning electric and thermal plants, water treatment facilities, and essential health and transportation systems, robust security has never been more urgent. The fourth industrial revolution has broadened the attack surface, making anomaly detection in Industrial Control Systems (ICS) a paramount concern for maintaining operational integrity. This research delves into the potential of cutting-edge deep learning techniques like CNNs, LSTM networks, AE, linear models (LIN), Gated Recurrent Units (GRU), and DNN—to effectively identify anomalies within the ICS environment using the SWaT dataset. Each approach underwent rigorous evaluation based on critical performance metrics such as accuracy, precision, recall, and F1 score. Through insightful visualizations of confusion matrices, we reveal the intricacies of model decision-making, including the nature of false positives and negatives. Our findings highlight the capabilities of advanced neural networks for anomaly detection and lay the groundwork for implementing robust security measures, enhancing the resilience of industrial systems against emerging threats. This work is a significant step toward safeguarding our vital infrastructure.