Distributed Outlier Detection Method in ICS Based on Improved Self-Adaptive Deep Federating Learning
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.
distributed control systemdeep learningjoint learningedge computing
国家科技期刊平台
NSTL
万方数据
