Cyber-Resilient Digital Twins: Discriminating Attacks for Safe Critical Infrastructure Control

• URL: http://arxiv.org/abs/2603.18613v1
• Date: Thu, 19 Mar 2026 08:33:21 GMT
• Title: Cyber-Resilient Digital Twins: Discriminating Attacks for Safe Critical Infrastructure Control
• Abstract summary: i-SDT combines hydraulically-regularized predictive modelling, multi-class attack discrimination, and adaptive resilient control.<n> Evaluation on SWaT and WADI datasets shows major gains in detection accuracy, 44.1% fewer false alarms, and 56.3% lower operational costs.
• Score: 0.0815557531820863
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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