Anomaly Detection in Industrial Control Systems using Machine Learning Techniques

Abstract

Machine learning techniques are being widely used to identify and respond to unusual events in industrial controls systems (ICS), where they play a vital role in preventing potential catastrophes. This paper reviews the various techniques that are used in anomaly detection in these systems. The paper discusses the definition of an anomaly detection process and provides a comprehensive review of the various techniques involved in this area. It also explores the applications of machine learning and statistical techniques in this domain. Some of the techniques that are commonly used in this area include clustering, decision trees and random forests, and control charts. The paper also covers the applications and challenges of anomaly detection in different industrial control systems such as water treatment plants, power grid systems, and chemical plants. Case studies are presented to demonstrate the effectiveness of learning-based techniques in identifying anomalies in these facilities. The paper also presents an evaluation of the performance of various machine learning techniques in performing anomaly detection. The evaluation metrics that are used in these experiments include false positive rate, accuracy, recall, area under receiver characteristic curve, and F1 score. The paper concludes by providing a summary of the findings of the review and the future directions of the investigation in anomaly detection for industrial control systems. The paper offers valuable insights into the latest state-of-art techniques in this area, and it can help practitioners and researchers make informed decisions when it comes to choosing the appropriate ones for their specific projects.