Using datasets from industrial control systems for cyber security research and education

Qin Lin; Sicco Verwer; Robert Kooij; Aditya Mathur

doi:10.1007/978-3-030-37670-3_10

Using datasets from industrial control systems for cyber security research and education

Qin Lin, Sicco Verwer, Robert Kooij^*, Aditya Mathur

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

77 Downloads (Pure)

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.

Original language	English
Title of host publication	Critical Information Infrastructures Security - 14th International Conference, CRITIS 2019, Revised Selected Papers
Editors	Simin Nadjm-Tehrani
Publisher	Springer
Pages	122-133
Number of pages	12
Volume	11777
ISBN (Print)	9783030376697
DOIs	https://doi.org/10.1007/978-3-030-37670-3_10
Publication status	Published - 2020
Event	14th International Conference on Critical Information Infrastructures Security, CRITIS 2019 - Linköping, Sweden Duration: 23 Sept 2019 → 25 Sept 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11777 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Critical Information Infrastructures Security, CRITIS 2019
Country/Territory	Sweden
City	Linköping
Period	23/09/19 → 25/09/19

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Anomaly detection
Cyber Data Analytics
Cyber security
Cyber-physical systems
Industrial Control Systems
Research and education

Access to Document

10.1007/978-3-030-37670-3_10

Lin2020_Chapter_UsingDatasetsFromIndustrialConFinal published version, 365 KB

Cite this

Lin, Q., Verwer, S., Kooij, R., & Mathur, A. (2020). Using datasets from industrial control systems for cyber security research and education. In S. Nadjm-Tehrani (Ed.), Critical Information Infrastructures Security - 14th International Conference, CRITIS 2019, Revised Selected Papers (Vol. 11777, pp. 122-133). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11777 LNCS). Springer. https://doi.org/10.1007/978-3-030-37670-3_10

Lin, Qin ; Verwer, Sicco ; Kooij, Robert et al. / Using datasets from industrial control systems for cyber security research and education. Critical Information Infrastructures Security - 14th International Conference, CRITIS 2019, Revised Selected Papers. editor / Simin Nadjm-Tehrani. Vol. 11777 Springer, 2020. pp. 122-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Lin, Q, Verwer, S , Kooij, R & Mathur, A 2020, Using datasets from industrial control systems for cyber security research and education. in S Nadjm-Tehrani (ed.), Critical Information Infrastructures Security - 14th International Conference, CRITIS 2019, Revised Selected Papers. vol. 11777, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11777 LNCS, Springer, pp. 122-133, 14th International Conference on Critical Information Infrastructures Security, CRITIS 2019, Linköping, Sweden, 23/09/19. https://doi.org/10.1007/978-3-030-37670-3_10

Using datasets from industrial control systems for cyber security research and education. / Lin, Qin; Verwer, Sicco ; Kooij, Robert et al.
Critical Information Infrastructures Security - 14th International Conference, CRITIS 2019, Revised Selected Papers. ed. / Simin Nadjm-Tehrani. Vol. 11777 Springer, 2020. p. 122-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11777 LNCS).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Lin, Qin

AU - Verwer, Sicco

AU - Kooij, Robert

AU - Mathur, Aditya

N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

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KW - Cyber-physical systems

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M3 - Conference contribution

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SN - 9783030376697

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Lin Q, Verwer S , Kooij R, Mathur A. Using datasets from industrial control systems for cyber security research and education. In Nadjm-Tehrani S, editor, Critical Information Infrastructures Security - 14th International Conference, CRITIS 2019, Revised Selected Papers. Vol. 11777. Springer. 2020. p. 122-133. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-37670-3_10

Using datasets from industrial control systems for cyber security research and education

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Keywords

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