Challenges of machine learning based monitoring for industrial control system networks

Matti Mantere; Ilkka Uusitalo; Mirko Sailio; Sami Noponen

doi:10.1109/WAINA.2012.135

Challenges of machine learning based monitoring for industrial control system networks

Matti Mantere, Ilkka Uusitalo, Mirko Sailio, Sami Noponen

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

28 Citations (Scopus)

Abstract

Detecting network intrusions and anomalies in industrial control systems is growing in urgency. Such systems used to be isolated but are now being connected to the outside world. Even in the case of isolated networks, privileged users may still present various threats to the system, either accidentally or intentionally. Also malfunctions in devices may cause anomalous traffic. Anomaly detection based network monitoring and intrusion detection systems could be capable of discerning normal and aberrant traffic in industrial control systems, detecting security incidents in an early phase. In this paper we discuss the challenges for such a monitoring system. One of the challenges is which features best differentiate between anomalous and normal behaviour. In the analysis, special focus is placed on this selection

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	26th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012
Place of Publication	Fukuoka, Japan
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	968-972
ISBN (Print)	978-1-4673-0867-0, 978-0-7695-4652-0
DOIs	https://doi.org/10.1109/WAINA.2012.135
Publication status	Published - 2012
MoE publication type	Not Eligible
Event	26th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012 - Fukuoka, Japan Duration: 26 Mar 2012 → 29 Mar 2012

Conference

Conference	26th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012
Abbreviated title	WAINA 2012
Country/Territory	Japan
City	Fukuoka
Period	26/03/12 → 29/03/12

Keywords

Industrial control
intrusion detection
machine learning
monitoring
production facilities
protocols

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/WAINA.2012.135

Cite this

Mantere, M., Uusitalo, I., Sailio, M., & Noponen, S. (2012). Challenges of machine learning based monitoring for industrial control system networks. In Proceedings: 26th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012 (pp. 968-972). IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/WAINA.2012.135

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title = "Challenges of machine learning based monitoring for industrial control system networks",

abstract = "Detecting network intrusions and anomalies in industrial control systems is growing in urgency. Such systems used to be isolated but are now being connected to the outside world. Even in the case of isolated networks, privileged users may still present various threats to the system, either accidentally or intentionally. Also malfunctions in devices may cause anomalous traffic. Anomaly detection based network monitoring and intrusion detection systems could be capable of discerning normal and aberrant traffic in industrial control systems, detecting security incidents in an early phase. In this paper we discuss the challenges for such a monitoring system. One of the challenges is which features best differentiate between anomalous and normal behaviour. In the analysis, special focus is placed on this selection",

keywords = "Industrial control, intrusion detection, machine learning, monitoring, production facilities, protocols",

author = "Matti Mantere and Ilkka Uusitalo and Mirko Sailio and Sami Noponen",

note = "Project code: 41465 ; 26th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012, WAINA 2012 ; Conference date: 26-03-2012 Through 29-03-2012",

year = "2012",

doi = "10.1109/WAINA.2012.135",

language = "English",

isbn = "978-1-4673-0867-0",

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booktitle = "Proceedings",

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Mantere, M, Uusitalo, I, Sailio, M & Noponen, S 2012, Challenges of machine learning based monitoring for industrial control system networks. in Proceedings: 26th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012. IEEE Institute of Electrical and Electronic Engineers, Fukuoka, Japan, pp. 968-972, 26th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012, Fukuoka, Japan, 26/03/12. https://doi.org/10.1109/WAINA.2012.135

Challenges of machine learning based monitoring for industrial control system networks. / Mantere, Matti; Uusitalo, Ilkka; Sailio, Mirko et al.
Proceedings: 26th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012. Fukuoka, Japan: IEEE Institute of Electrical and Electronic Engineers, 2012. p. 968-972.

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

TY - GEN

T1 - Challenges of machine learning based monitoring for industrial control system networks

AU - Mantere, Matti

AU - Uusitalo, Ilkka

AU - Sailio, Mirko

AU - Noponen, Sami

N1 - Project code: 41465

PY - 2012

Y1 - 2012

N2 - Detecting network intrusions and anomalies in industrial control systems is growing in urgency. Such systems used to be isolated but are now being connected to the outside world. Even in the case of isolated networks, privileged users may still present various threats to the system, either accidentally or intentionally. Also malfunctions in devices may cause anomalous traffic. Anomaly detection based network monitoring and intrusion detection systems could be capable of discerning normal and aberrant traffic in industrial control systems, detecting security incidents in an early phase. In this paper we discuss the challenges for such a monitoring system. One of the challenges is which features best differentiate between anomalous and normal behaviour. In the analysis, special focus is placed on this selection

AB - Detecting network intrusions and anomalies in industrial control systems is growing in urgency. Such systems used to be isolated but are now being connected to the outside world. Even in the case of isolated networks, privileged users may still present various threats to the system, either accidentally or intentionally. Also malfunctions in devices may cause anomalous traffic. Anomaly detection based network monitoring and intrusion detection systems could be capable of discerning normal and aberrant traffic in industrial control systems, detecting security incidents in an early phase. In this paper we discuss the challenges for such a monitoring system. One of the challenges is which features best differentiate between anomalous and normal behaviour. In the analysis, special focus is placed on this selection

KW - Industrial control

KW - intrusion detection

KW - machine learning

KW - monitoring

KW - production facilities

KW - protocols

U2 - 10.1109/WAINA.2012.135

DO - 10.1109/WAINA.2012.135

M3 - Conference article in proceedings

SN - 978-1-4673-0867-0

SN - 978-0-7695-4652-0

SP - 968

EP - 972

BT - Proceedings

PB - IEEE Institute of Electrical and Electronic Engineers

CY - Fukuoka, Japan

T2 - 26th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012

Y2 - 26 March 2012 through 29 March 2012

ER -

Challenges of machine learning based monitoring for industrial control system networks

Abstract

Conference

Keywords

UN SDGs

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