Multi-scale hierarchical causality-inspired graph network for interpretable anomaly detection in industrial time series
发布时间：2026-09-22 点击次数：
姚羽（教授）
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- 博士生导师 硕士生导师
- 所在单位：计算机科学与工程学院
- 职务：复杂网络系统安全保障技术教育部工程研究中心主任
- 学历：博士研究生毕业
- 性别：男
- 联系方式：
- 学位：博士
- 毕业院校：东北大学
- 所属院系：计算机科学与工程学院
- 学科：
计算机应用技术
计算机软件与理论
计算机系统结构
- 论文名称：Multi-scale hierarchical causality-inspired graph network for interpretable anomaly detection in industrial time series
- 发表刊物：Engineering Applications of Artificial Intelligence
- 影响因子：9.0
- 摘要：Anomaly detection in Industrial Control Systems (ICS) is vital for safety but challenged by complex data characteristics (multi-scale dynamics, hierarchical structures, time-varying dependencies, label scarcity) that impede existing methods. This paper introduces the Multi-Scale Causality-Inspired Convolutional Attention Network (MC-GCAN), a novel Interpretable framework for high-precision, interpretable ICS anomaly detection. MC-GCAN integrates hierarchical multi-scale feature learning for diverse temporal patterns and dependency-aware dynamic graph learning for adaptive sensor dependencies. It employs a refined dual-layer adaptive thresholding strategy for node/system-level anomaly determination, enhanced by contrastive learning for subtle anomalies. Extensive experiments on two benchmark datasets indicate that MC-GCAN achieves competitive performance compared to several state-of-the-art methods, demonstrating effective results and good generalization ability. Crucially, the framework demonstrates significant interpretability; validation confirms its ability to provide insights from system down to individual sensor levels. This node-level anomaly attribution holds substantial practical engineering value for root cause analysis. MC-GCAN offers a robust, effective, and interpretable solution for enhancing ICS security and reliability.
- 关键字：Industrial control systems; Anomaly detection; Graph Neural Network; Multivariate time series; Adaptive thresholding
- 论文类型：SCI JCR Q1
- 备注：https://www.sciencedirect.com/science/article/abs/pii/S0952197626015782
- 学科门类：工学
- 文献类型：JCR 一区
- 一级学科：计算机科学与技术
- 是否译文：否