Analysis of Industrial Control Systems Traffic Based on Time Series

Abstract

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