Using datasets from industrial control systems for cyber security research and education
Q. Lin (TU Delft - Cyber Security)
S.E. Verwer (TU Delft - Cyber Security)
Robert E. Kooij (Singapore University of Technology and Design, TU Delft - Network Architectures and Services)
Aditya Mathur (Purdue University, Singapore University of Technology and Design)
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Abstract
The availability of high-quality benchmark datasets is an important prerequisite for research and education in the cyber security domain. Datasets from realistic systems offer a platform for researchers to develop and test novel models and algorithms. Such datasets also offer students opportunities for active and project-centric learning. In this paper, we describe six publicly available datasets from the domain of Industrial Control Systems (ICS). Five of these datasets are obtained through experiments conducted in the context of operational ICS while the sixth is obtained from a widely used simulation tool, namely EPANET, for large scale water distribution networks. This paper presents two studies on the use of the datasets. The first study uses the dataset from a live water treatment plant. This study leads to a novel and explainable anomaly detection method based upon Timed Automata and Bayesian Networks. The study conducted in the context of education made use of the water distribution network dataset in a graduate course on cyber data analytics. Through an assignment, students explored the effectiveness of various methods for anomaly detection. Research outcomes and the success of the course indicate an appreciation in the research community and positive learning experience in education.