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dc.contributor.authorChockalingam, Sabarathinam
dc.contributor.authorPieters, Wolter
dc.contributor.authorTeixeira, André
dc.contributor.authorvan Gelder, Pieter
dc.date.accessioned2021-12-06T15:12:53Z
dc.date.available2021-12-06T15:12:53Z
dc.date.created2021-09-01T04:34:51Z
dc.date.issued2021
dc.identifier.citationCybersecurity. 2021, 4 1-19.en_US
dc.identifier.issn2523-3246
dc.identifier.urihttps://hdl.handle.net/11250/2833013
dc.description.abstractWater management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems (ICS). These systems are becoming more connected to the internet, either directly or through the corporate networks. This makes them vulnerable to cyber-attacks. Abnormal behaviour in floodgates operated by ICS could be caused by both (intentional) attacks and (accidental) technical failures. When operators notice abnormal behaviour, they should be able to distinguish between those two causes to take appropriate measures, because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector. In the previous work, we developed the attack-failure distinguisher framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two causes, including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models. As a full case study of the attack-failure distinguisher framework, this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates, addressing the problem of floodgate operators. We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model. The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements. This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectBayesiansk Tiltro Nettverken_US
dc.subjectBayesian Belief Networken_US
dc.subjectSikkerheten_US
dc.subjectSafetyen_US
dc.subjectKunnskapsakkvisisjonen_US
dc.subjectKnowledge acquisitionen_US
dc.subjectVannforvaltningen_US
dc.subjectWater managmenten_US
dc.subjectTrygden_US
dc.subjectSecurityen_US
dc.subjectAngrepshåndteringen_US
dc.subjectAttack Mitigationen_US
dc.subjectCyber securityen_US
dc.subjectCyber securityen_US
dc.titleBayesian Network Model to Distinguish between Intentional Attacks and Accidental Technical Failures: A Case Study of Floodgatesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© The Author(s). 2021en_US
dc.subject.nsiVDP::Sikkerhet og sårbarhet: 424en_US
dc.subject.nsiVDP::Security and vulnerability: 424en_US
dc.source.pagenumber1-19en_US
dc.source.volume4en_US
dc.source.journalCybersecurityen_US
dc.identifier.doi10.1186/s42400-021-00086-6
dc.identifier.cristin1930276
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextoriginal
cristin.qualitycode1


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