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Conference Paper: Detecting Anomalous Behavior of PLC using Semi-supervised Machine Learning

TitleDetecting Anomalous Behavior of PLC using Semi-supervised Machine Learning
Authors
Issue Date2017
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1803058
Citation
IEEE Conference on Communications and Network Security (CNS 2017), Las Vegas, NV, USA, 9-11 October 2017, p. 580-585 How to Cite?
AbstractIndustrial Control System (ICS) is used to monitor and control critical infrastructures. Programmable logic controllers (PLCs) are major components of ICS, which are used to form automation system. It is important to protect PLCs from any attacks and undesired incidents. However, it is not easy to apply traditional tools and techniques to PLCs for security protection and forensics because of its unique architectures. Semi-supervised machine learning algorithm, One-class Support Vector Machine (OCSVM), has been applied successfully to many anomaly detection problems. This paper proposes a novel methodology to detect anomalous events of PLC by using OCSVM. The methodology was applied to a simulated traffic light control system to illustrate its effectiveness and accuracy. Our results show that high accuracy of identification of anomalous PLC operations is obtained which can help investigators to perform PLC forensics efficiently and effectively.
Persistent Identifierhttp://hdl.handle.net/10722/244438

 

DC FieldValueLanguage
dc.contributor.authorYau, KK-
dc.contributor.authorChow, KP-
dc.contributor.authorYiu, SM-
dc.contributor.authorChan, CF-
dc.date.accessioned2017-09-18T01:52:26Z-
dc.date.available2017-09-18T01:52:26Z-
dc.date.issued2017-
dc.identifier.citationIEEE Conference on Communications and Network Security (CNS 2017), Las Vegas, NV, USA, 9-11 October 2017, p. 580-585-
dc.identifier.urihttp://hdl.handle.net/10722/244438-
dc.description.abstractIndustrial Control System (ICS) is used to monitor and control critical infrastructures. Programmable logic controllers (PLCs) are major components of ICS, which are used to form automation system. It is important to protect PLCs from any attacks and undesired incidents. However, it is not easy to apply traditional tools and techniques to PLCs for security protection and forensics because of its unique architectures. Semi-supervised machine learning algorithm, One-class Support Vector Machine (OCSVM), has been applied successfully to many anomaly detection problems. This paper proposes a novel methodology to detect anomalous events of PLC by using OCSVM. The methodology was applied to a simulated traffic light control system to illustrate its effectiveness and accuracy. Our results show that high accuracy of identification of anomalous PLC operations is obtained which can help investigators to perform PLC forensics efficiently and effectively.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1803058-
dc.relation.ispartofIEEE Conference on Communications and Network Security (CNS)-
dc.rightsIEEE Conference on Communications and Network Security (CNS). Copyright © IEEE.-
dc.titleDetecting Anomalous Behavior of PLC using Semi-supervised Machine Learning-
dc.typeConference_Paper-
dc.identifier.emailChow, KP: kpchow@hkucc.hku.hk-
dc.identifier.emailYiu, SM: smyiu@cs.hku.hk-
dc.identifier.authorityChow, KP=rp00111-
dc.identifier.authorityYiu, SM=rp00207-
dc.identifier.doi10.1109/CNS.2017.8228713-
dc.identifier.hkuros278079-
dc.identifier.spage580-
dc.identifier.epage585-
dc.publisher.placeUnited States-

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