Feature selection for machine learning based anomaly detection in industrial control system networks

Matti Mantere; Mirko Sailio; Sami Noponen

doi:10.1109/GreenCom.2012.127

Feature selection for machine learning based anomaly detection in industrial control system networks

Matti Mantere, Mirko Sailio, Sami Noponen

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

7 Citations (Scopus)

Abstract

The nature of the traffic in industrial control system network is markedly different from more open networks. Industrial control system networks should be far more restricted in what types of traffic diversity is present. This enables the usage of approaches that are currently not as feasible in open environments, such as machine learning based anomaly detection. Without proper customization for the special requirements of industrial control system network environment many existing anomaly or misuse detection systems will perform sub-optimally. Machine learning based approach would reduce the amount of manual customization required for different restricted network environments of which an industrial control system network is an good example of. In this paper we present an initial analysis of data received from a ethernet network of a live running industrial site. This includes both control data and the data flowing between the control network and the office network. A set of possible features to be used for detecting anomalies is studied for this environment.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	771-774
ISBN (Electronic)	978-0-7695-4865-4
ISBN (Print)	978-1-4673-5146-1
DOIs	https://doi.org/10.1109/GreenCom.2012.127
Publication status	Published - 2012
MoE publication type	Not Eligible
Event	IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012 - Besancon, France Duration: 20 Nov 2012 → 23 Nov 2012

Conference

Conference	IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012
Abbreviated title	GreenCom 2012
Country	France
City	Besancon
Period	20/11/12 → 23/11/12

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10.1109/GreenCom.2012.127

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Mantere, M., Sailio, M., & Noponen, S. (2012). Feature selection for machine learning based anomaly detection in industrial control system networks. In Proceedings: IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012 (pp. 771-774). IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/GreenCom.2012.127

Mantere, Matti ; Sailio, Mirko ; Noponen, Sami. / Feature selection for machine learning based anomaly detection in industrial control system networks. Proceedings: IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012. IEEE Institute of Electrical and Electronic Engineers, 2012. pp. 771-774

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title = "Feature selection for machine learning based anomaly detection in industrial control system networks",

abstract = "The nature of the traffic in industrial control system network is markedly different from more open networks. Industrial control system networks should be far more restricted in what types of traffic diversity is present. This enables the usage of approaches that are currently not as feasible in open environments, such as machine learning based anomaly detection. Without proper customization for the special requirements of industrial control system network environment many existing anomaly or misuse detection systems will perform sub-optimally. Machine learning based approach would reduce the amount of manual customization required for different restricted network environments of which an industrial control system network is an good example of. In this paper we present an initial analysis of data received from a ethernet network of a live running industrial site. This includes both control data and the data flowing between the control network and the office network. A set of possible features to be used for detecting anomalies is studied for this environment.",

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Mantere, M, Sailio, M & Noponen, S 2012, Feature selection for machine learning based anomaly detection in industrial control system networks. in Proceedings: IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012. IEEE Institute of Electrical and Electronic Engineers, pp. 771-774, IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012, Besancon, France, 20/11/12. https://doi.org/10.1109/GreenCom.2012.127

Feature selection for machine learning based anomaly detection in industrial control system networks. / Mantere, Matti; Sailio, Mirko ; Noponen, Sami.

Proceedings: IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012. IEEE Institute of Electrical and Electronic Engineers, 2012. p. 771-774.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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Mantere M, Sailio M , Noponen S. Feature selection for machine learning based anomaly detection in industrial control system networks. In Proceedings: IEEE International Conference on Green Computing and Communications, Conference on Internet of Things, and Conference on Cyber, Physical and Social Computing GreenCom 2012. IEEE Institute of Electrical and Electronic Engineers. 2012. p. 771-774 https://doi.org/10.1109/GreenCom.2012.127