AI 中文总结
针对网络物理系统多变量传感器时间序列异常检测，提出DPR-GM框架，利用大语言模型提取传感器对物理耦合，经皮尔逊相关性调制和传感器级可靠性加权，在SKAB基准测试中优于多种基线。
AI 中文摘要
多变量传感器时间序列的异常检测对于网络物理系统（CPS）的工业监测至关重要，即使是与正常行为的细微偏差也可能表明过程中断。最近基于图的方法取得了显著进展，但在标记异常稀缺且正常数据有限的小规模物理系统中往往存在困难。我们提出了DPR-GM（域先验正则化图建模），这是一个基于预测的框架，将系统设计知识纳入图构建。DPR-GM利用大语言模型从系统文档中提取传感器对之间的定向物理耦合，编码为二进制域邻接矩阵作为传感器关系的结构门。然后通过从正常训练数据估计的皮尔逊相关性对该门进行调制。异常分数进一步由从变异系数导出的传感器级可靠性加权。所有图和加权组件在训练前固定，不添加可学习参数。在SKAB基准测试中，DPR-GM在F1、AUROC和AUPRC方面优于基于图、统计和深度学习的基线，表明域结构化图先验是数据稀缺的CPS中完全学习拓扑的实用替代方案。
英文摘要
Anomaly detection on multivariate sensor time series is critical for industrial monitoring of cyber-physical systems (CPS), where even subtle deviations from normal behavior can indicate process disruption. Recent graph-based approaches have made significant progress, but they often struggle in small-scale physical systems with scarce labeled anomalies and limited normal data. In such settings, graph-based models tend to capture spurious correlations and produce unstable sensor topologies. We propose DPR-GM (Domain-Prior-Regularized Graph Modeling), a forecasting-based framework that incorporates system design knowledge into graph construction. DPR-GM leverages a large language model (LLM) to extract directed physical couplings between sensor pairs from system documentation, which are encoded as a binary domain adjacency matrix serving as a structural gate over sensor relations. This gate is then modulated by Pearson correlations estimated from normal training data. The anomaly score is further weighted by sensor-level reliability derived from the coefficient of variation. All graph and weighting components are fixed prior to training and add no learnable parameters. On the SKAB benchmark, DPR-GM outperforms graph-based, statistical, and deep learning baselines across F1, AUROC, and AUPRC, showing that domain-structured graph priors are a practical alternative to fully learned topologies in data-scarce CPS.
Comments12 pages, ICML 2026 AI for Science Workshop
