Deep Learning Techniques for Intrusion Detection in Critical Infrastructure
Pankaj Bhambri, Ilona Pawełoszek · 2025
The increase in cyber threats aimed at critical infrastructure systems highlights the necessity for sophisticated intrusion detection systems (IDSs). This chapter examines the utilization of deep learning methodologies for intrusion detection in critical infrastructure, emphasizing the distinct challenges presented by these systems, including heterogeneity, scalability, and the necessity for real-time responses. The chapter commences with a summary of essential infrastructure and the changing cyber threat environment. It subsequently analyzes the contribution of deep learning to improving the precision and efficacy of IDSs through the utilization of techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Case studies and practical applications demonstrate the efficacy of these techniques in detecting complex attack patterns, including advanced persistent threats (APTs) and zero-day exploits. The chapter additionally examines practical considerations, encompassing computational complexity, data accessibility, and model interpretability. Ultimately, the discussion focuses on emerging trends and future directions, highlighting the incorporation of explainable artificial intelligence (XAI), federated learning, and hybrid models to enhance the robustness, adaptability, and transparency of intrusion detection in critical infrastructure.