Modification of Recursive Kalman Filter Algorithm for Adaptive Prediction of Cyber Resilience for Industrial Systems

This paper describes an approach to modification of the recursive Kalman filter algorithm to obtain adaptive prediction of time series from industrial systems. To ensure cyber resilience of modern industrial systems, it is necessary to detect anomalies in their work at an early stage. For this, data from industrial systems are presented as time series, and an adaptive prediction model combined with machine learning classification algorithm applies to identify anomalies. The effectiveness of the proposed approach is confirmed experimentally.