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This paper introduces i-SDT, an intelligent self-defending digital twin for industrial cyber-physical systems that combines hydraulically-regularized temporal convolutional networks (TCNs) for predictive modeling with a recurrent residual encoder using Maximum Mean Discrepancy (MMD) for multi-class attack discrimination. i-SDT uses Model Predictive Control (MPC) with uncertainty-aware predictions to maintain safe operations without full system shutdowns when attacks are detected. Experiments on SWaT and WADI datasets demonstrate improved detection accuracy, reduced false alarms, and lower operational costs, highlighting the potential for real-time autonomous cyber-physical defense.
Digital twins can now discriminate between different types of cyberattacks on critical infrastructure, enabling targeted responses instead of costly full shutdowns.
Industrial Cyber-Physical Systems (ICPS) face growing threats from cyber-attacks that exploit sensor and control vulnerabilities. Digital Twin (DT) technology can detect anomalies via predictive modelling, but current methods cannot distinguish attack types and often rely on costly full-system shutdowns. This paper presents i-SDT (intelligent Self-Defending DT), combining hydraulically-regularized predictive modelling, multi-class attack discrimination, and adaptive resilient control. Temporal Convolutional Networks (TCNs) with differentiable conservation constraints capture nominal dynamics and improve robustness to adversarial manipulations. A recurrent residual encoder with Maximum Mean Discrepancy (MMD) separates normal operation from single- and multi-stage attacks in latent space. When attacks are confirmed, Model Predictive Control (MPC) uses uncertainty-aware DT predictions to keep operations safe without shutdown. Evaluation on SWaT and WADI datasets shows major gains in detection accuracy, 44.1% fewer false alarms, and 56.3% lower operational costs in simulation-in-the-loop evaluation. with sub-second inference latency confirming real-time feasibility on plant-level workstations, i-SDT advances autonomous cyber-physical defense while maintaining operational resilience.
