Detection of Threats in Cyberphysical Systems Based on Deep Learning Methods Using Multidimensional Time Series

Abstract

A method for detecting anomalies in the work of cyberphysical systems by analyzing a multidimensional time series is proposed. The method is based on the use of neural network technologies to predict the values ​​of the time series of the system data and to identify deviations between the predicted value and the current data obtained from the sensors and actuators. The results of experimental studies are presented, which testify to the effectiveness of the proposed solution.