Anomaly detection in wastewater treatment process for cyber resilience risks evaluation

Timely detection and prevention of violations in the technological process of wastewater treatment caused by threats of different nature is a highly relevant research problem. Modern systems are equipped with a large number of technological sensors. Data from these sensors can be used to detect anomalies in the technological process. Their timely detection, prediction and processing ensures the continuity and fault tolerance of the technological process. The aim of the research is to improve the accuracy of detection of such anomalies. We propose a methodology for the identification and subsequent assessment of cyber resilience risks of the wastewater treatment process, which includes the distinctive procedure of training dataset generation and the anomaly detection based on deep learning methods. The availability of training datasets is a necessary condition for the efficient application of the proposed technology. A distinctive feature of the anomaly detection approach is a new method of processing input sensor data, which allows the use of computationally efficient analytical models with high accuracy of anomaly detection, and outperforms the efficiency of previously published methods.