A supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system

Industrial Control Systems are part of our daily life in industries such as transportation, water, gas, oil, smart cities, and telecommunications. Technological development over time have improved their components including operating system platforms, hardware capabilities, and connectivity with networks inside and outside the organization. Consequently, the Industrial Control Systems components are exposed to sophisticated threats with weak security mechanism in place. This paper proposes a supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system. A testbed of such a system is implemented using the Festo MPA Control Process Rig. The machine-learning algorithms, which include SVN, KNN, and Random Forest, perform classification tasks process in three different datasets obtained from the testbed. The algorithms are compared in terms of accuracy and F-measure. The results show that Random Forest achieves 5% better performance over KNN and SVM with small datasets and 4% regarding large datasets. For the time taken to build the model, KNN presents the best performance. However, its difference with Random Forest is smaller than with SVM.