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.

翻译：工业信息物理系统（ICPS）面临利用传感器与控制漏洞的网络攻击日益增长威胁。数字孪生（DT）技术通过预测建模检测异常，但现有方法无法区分攻击类型且常依赖成本高昂的全系统停机。本文提出i-SDT（智能自防御数字孪生），融合水力正则化预测建模、多类别攻击判别及自适应韧性控制。采用具有可微守恒约束的时间卷积网络（TCNs）捕获标称动态特性，提升对对抗性操控的鲁棒性。基于最大均值差异（MMD）的循环残差编码器在潜在空间中分离正常工况与单阶段/多阶段攻击。当攻击确认后，模型预测控制（MPC）利用不确定性感知的数字孪生预测保障运行安全，避免停机。在SWaT与WADI数据集上的评估表明：检测精度显著提升，虚警率降低44.1%，环内仿真测试中运营成本下降56.3%。亚秒级推理延迟验证了工厂级工作站实时可行性，i-SDT在保持运行韧性的同时推进了自主信息物理防御发展。