Building a Forecast Using a Linear Prediction Filter for the Purpose of Detecting Anomalies in the Signals of Automated Process Control Systems

Forecasting technology plays an important role in the construction of systems for detecting anomalies in dynamic data flows of automated process control systems (APCS) resulting from the impact of cyberattacks. To form a forecast of the studied signals, methods for forming a single-component forecast and a multi-component forecast of an information signal using a linear prediction digital filter are considered. It is shown that for the detection of anomalies in the observed signals of APCS, the predictor prediction error signal implemented using a linear prediction filter is informative. The high information content of the use of spectral analysis of the prediction error signal in detecting anomalies in the observed signals of automated process control systems is shown.