Multi-Level Graph Attention Network-Based Anomaly Detection in Industrial Control System
Industrial control systems (ICSs) are vital to critical infrastructure in energy, manufacturing, and other industries. As ICSs become increasingly interconnected, their complexity grows, making them more vulnerable to cyber attacks and system failures. This growing complexity underscores the critica...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/14/5/210 |
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| author | Longxin Lin Anyang Gu Feiyan Min Shan Zhou |
| author_facet | Longxin Lin Anyang Gu Feiyan Min Shan Zhou |
| author_sort | Longxin Lin |
| collection | DOAJ |
| description | Industrial control systems (ICSs) are vital to critical infrastructure in energy, manufacturing, and other industries. As ICSs become increasingly interconnected, their complexity grows, making them more vulnerable to cyber attacks and system failures. This growing complexity underscores the critical need for advanced anomaly detection techniques to ensure the safe and reliable operation of ICSs. To address this need, we propose a novel method, the physical process and controller graph attention network (PCGAT), which constructs multi-level graphs based on physical process and controller information. Experiments on two real-world ICS datasets demonstrate that PCGAT achieves superior performance and enables the localization of anomalies within specific physical processes. Moreover, by leveraging graph attention networks (GATs), PCGAT enhances interpretability in anomaly detection. |
| format | Article |
| id | doaj-art-51d9e1452c8e4c76bcfa163786eb159e |
| institution | OA Journals |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-51d9e1452c8e4c76bcfa163786eb159e2025-08-20T02:33:35ZengMDPI AGActuators2076-08252025-04-0114521010.3390/act14050210Multi-Level Graph Attention Network-Based Anomaly Detection in Industrial Control SystemLongxin Lin0Anyang Gu1Feiyan Min2Shan Zhou3College of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaSchool of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, ChinaCollege of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaCollege of Information Science and Technology, Jinan University, Guangzhou 510632, ChinaIndustrial control systems (ICSs) are vital to critical infrastructure in energy, manufacturing, and other industries. As ICSs become increasingly interconnected, their complexity grows, making them more vulnerable to cyber attacks and system failures. This growing complexity underscores the critical need for advanced anomaly detection techniques to ensure the safe and reliable operation of ICSs. To address this need, we propose a novel method, the physical process and controller graph attention network (PCGAT), which constructs multi-level graphs based on physical process and controller information. Experiments on two real-world ICS datasets demonstrate that PCGAT achieves superior performance and enables the localization of anomalies within specific physical processes. Moreover, by leveraging graph attention networks (GATs), PCGAT enhances interpretability in anomaly detection.https://www.mdpi.com/2076-0825/14/5/210industrial control systemsanomaly detectiongraph neural networksensors and actuators |
| spellingShingle | Longxin Lin Anyang Gu Feiyan Min Shan Zhou Multi-Level Graph Attention Network-Based Anomaly Detection in Industrial Control System Actuators industrial control systems anomaly detection graph neural network sensors and actuators |
| title | Multi-Level Graph Attention Network-Based Anomaly Detection in Industrial Control System |
| title_full | Multi-Level Graph Attention Network-Based Anomaly Detection in Industrial Control System |
| title_fullStr | Multi-Level Graph Attention Network-Based Anomaly Detection in Industrial Control System |
| title_full_unstemmed | Multi-Level Graph Attention Network-Based Anomaly Detection in Industrial Control System |
| title_short | Multi-Level Graph Attention Network-Based Anomaly Detection in Industrial Control System |
| title_sort | multi level graph attention network based anomaly detection in industrial control system |
| topic | industrial control systems anomaly detection graph neural network sensors and actuators |
| url | https://www.mdpi.com/2076-0825/14/5/210 |
| work_keys_str_mv | AT longxinlin multilevelgraphattentionnetworkbasedanomalydetectioninindustrialcontrolsystem AT anyanggu multilevelgraphattentionnetworkbasedanomalydetectioninindustrialcontrolsystem AT feiyanmin multilevelgraphattentionnetworkbasedanomalydetectioninindustrialcontrolsystem AT shanzhou multilevelgraphattentionnetworkbasedanomalydetectioninindustrialcontrolsystem |