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

Robles-Durazno, Andres; Moradpoor, Naghmeh; McWhinnie, James; Russell, Gordon

Authors

Andres Robles-Durazno

James McWhinnie



Abstract

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.

Presentation Conference Type Conference Paper (Published)
Conference Name Cyber Security 2018: 2018 International Conference on Cyber Security and Protection of Digital Services
Start Date Jun 11, 2018
End Date Jun 12, 2018
Acceptance Date Apr 4, 2018
Online Publication Date Dec 6, 2018
Publication Date Dec 6, 2018
Deposit Date Apr 17, 2018
Publicly Available Date Apr 17, 2018
Publisher Institute of Electrical and Electronics Engineers
Book Title Proceedings of the IEEE International Conference on Cyber Security and Protection of Digital Services (Cyber Security 2018)
ISBN 978-1-5386-4683-0
DOI https://doi.org/10.1109/CyberSecPODS.2018.8560683
Keywords Industrial Control System, Energy Monitoring, SCADA, KNN, Random Forest, SVM, Anomaly Detection
Public URL http://researchrepository.napier.ac.uk/Output/1150035
Contract Date Apr 17, 2018

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