传感技术学报2024，Vol.37Issue(2)：241-255,15.DOI:10.3969/j.issn.1004-1699.2024.02.009
一种使用深度联合学习的ICS自适应异常检测方法
Distributed Outlier Detection Method in ICS Based on Improved Self-Adaptive Deep Federating Learning
摘要
Abstract
In order to improve the accuracy,timeliness and deployability of outlier detection method for industrial control systems,an adaptive anomaly detection method using deep joint learning in distributed control system is proposed.Specifically,a lightweight local learning model is proposed in the first place to improve the learning speed,make reasonable use of hardware resources,and ensure the feasibility of deployment in distributed edge devices.Secondly,an unsupervised learning model based only on normal data is pro-posed,and the detection mechanism is dynamically adjusted with kernel quantile estimation.Finally,the above methods are integrated into the joint learning framework,so that it can effectively carry out distributed outlier detection near the attack source in the edge seg-ment,so as to minimize the response time of the system to the abnormal attack.关键词
分布式控制系统/深度学习/联合学习/边缘计算Key words
distributed control system/deep learning/joint learning/edge computing分类
计算机与自动化引用本文复制引用
陈凤华,董金祥..一种使用深度联合学习的ICS自适应异常检测方法[J].传感技术学报,2024,37(2):241-255,15.基金项目
教育部产学合作协同育人项目(202101154036) （202101154036）
