Anomaly detection in Industrial Autonomous Decentralized System based on time series

With the ever growing demand of location-independent access to Autonomous Decentralized Systems (ADS), anomaly detection scheme for industrial Ethernet, which highly is satisfied with demanding real-time and reliable industrial applications, becomes one of the most pressing subjects in ADS. In this paper, we present an innovative approach to build a traffic model based on structural time series model for a chemical industry system. A basic structural model that decomposes time series into four items is established by the stationary analysis of industrial traffic. Parameters in the model are identified by the state space model, which is conducted from the training sequence using standard Kalman filter recursions and the EM algorithm. Furthermore, the performance of state space model is evaluated by the experimental results that confirm significant improvement in detection accuracy and the validity of abnormal data localization.