Abstract

Industrial Control Systems (ICS) encompassing Supervisory Control and Data Acquisition (SCADA) systems, Programmable Logic Controllers (PLC), and Distributed Control Systems (DCS) constitute the operational backbone of national critical infrastructures including electric power generation, water treatment, oil and gas refining, and chemical manufacturing. The digital transformation of these systems—characterized by convergence of operational technology with information technology, widespread IP-based networking, and cloud-connectivity—has exponentially increased attack surfaces accessible to sophisticated adversaries. Conventional signature-based intrusion detection systems, reliant upon known attack pattern databases, exhibit fundamental inadequacy against zero-day exploits, protocol-specific manipulation, and stealthy cyber-physical attacks that progressively degrade system integrity while evading deterministic alerts. This review presents a comprehensive engineering and computational framework for deep learning-based intrusion detection in ICS environments. We systematically analyze deep learning architectures optimized for industrial network traffic analysis—convolutional neural networks for spatial feature extraction from raw packet payloads, long short-term memory networks for temporal sequence modeling of control logic execution, autoencoders for unsupervised anomaly detection in high-dimensional sensor telemetry, and hybrid CNN-LSTM configurations for coordinated attack classification. Engineering design considerations encompassing real-time processing constraints, false positive impact mitigation, and operational technology security operations center integration are critically examined. Through translational evaluation of validated deployment cases—SCADA network protocol monitoring, electric substation intrusion detection, and smart manufacturing PLC protection—we synthesize evidenced performance outcomes: detection accuracy exceeding 98.5%, false positive rates below 0.5%, and inference latency compatible with closed-loop control timing requirements. Persistent challenges including adversarial evasion, imbalanced training data, model explainability for regulatory compliance, and secure lifecycle management are systematically analyzed. Future trajectories emphasize federated learning for cross-site collaborative defense without data exposure, graph neural networks for topology-aware attack propagation modeling, and autonomous cyber-physical self-healing architectures. This review provides control systems engineers, cybersecurity practitioners, and computational researchers with an integrated methodological foundation for engineering resilient, AI-secured industrial infrastructures.