提出可区分攻击类型的智能数字孪生系统，实现不中断运行下的安全防御。
Cyber-Resilient Digital Twins: Discriminating Attacks for Safe Critical Infrastructure Control
- 用带物理约束的时序卷积网络建模正常状态，提升对抗攻击鲁棒性。
- 通过隐空间分离技术识别单阶段与多阶段攻击，误报率降低44.1%。
- 结合不确定性感知控制策略，避免停机，适合工业关键设施实时防护。
工业网络物理系统面临日益严重的网络攻击威胁，尤其针对传感器与控制环节。数字孪生技术可通过预测建模检测异常，但现有方法难以区分攻击类型，常依赖代价高昂的全系统停机。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、多类攻击判别与自适应弹性控制。采用带有可微分守恒约束的时序卷积网络（TCNs）捕捉正常动态，增强对对抗性操纵的鲁棒性；基于最大均值差异（MMD）的循环残差编码器在隐空间中分离正常操作与单阶段、多阶段攻击。一旦确认攻击，模型预测控制（MPC）利用不确定性感知的数字孪生预测，在不中断运行的前提下保障安全。在SWaT与WADI数据集上的仿真验证显示，检测准确率显著提升，误报率降低44.1%，运营成本减少56.3%；推理延迟低于1秒，证实其在工厂级工作站上的实时可行性。i-SDT实现了自主的网络物理系统防御，同时保持运行韧性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。