| Title | An OpenPLC-based Active Real-time Anomaly Detection Framework for Industrial Control Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zheng, Chengxu, Wang, Xiaopeng, Luo, Xiaoyu, Fang, Chongrong, He, Jianping |
| Conference Name | 2022 China Automation Congress (CAC) |
| Keywords | Adaptation models, anomaly detection, Detectors, ICS Anomaly Detection, ICS security, industrial control, OpenPLC, pubcrawl, Real-time Systems, resilience, Resiliency, Scalability, security, Software, Task Analysis |
| Abstract | In recent years, the design of anomaly detectors has attracted a tremendous surge of interest due to security issues in industrial control systems (ICS). Restricted by hardware resources, most anomaly detectors can only be deployed at the remote monitoring ends, far away from the control sites, which brings potential threats to anomaly detection. In this paper, we propose an active real-time anomaly detection framework deployed in the controller of OpenPLC, which is a standardized open-source PLC and has high scalability. Specifically, we add adaptive active noises to control signals, and then identify a linear dynamic system model of the plant offline and implement it in the controller. Finally, we design two filters to process the estimated residuals based on the obtained model and use h2 detector for anomaly detection. Extensive experiments are conducted on an industrial control virtual platform to show the effectiveness of the proposed detection framework. |
| DOI | 10.1109/CAC57257.2022.10055121 |
| Citation Key | zheng_openplc-based_2022 |
An OpenPLC-based Active Real-time Anomaly Detection Framework for Industrial Control Systems