Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages

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dc.contributor.authorUstun, Taha Selim
dc.contributor.authorHussain, S. M. Suhail
dc.contributor.authorUlutas, Ahsen
dc.contributor.authorOnen, Ahmet
dc.contributor.authorRoomi, Muhammad M.
dc.contributor.authorMashima, Daisuke
dc.date.accessioned2024-02-23T14:35:21Z
dc.date.available2024-02-23T14:35:21Z
dc.date.issued2021
dc.departmentNEÜen_US
dc.description.abstractIncreased connectivity is required to implement novel coordination and control schemes. IEC 61850-based communication solutions have become popular due to many reasons-object-oriented modeling capability, interoperable connectivity and strong communication protocols, to name a few. However, communication infrastructure is not well-equipped with cybersecurity mechanisms for secure operation. Unlike online banking systems that have been running such security systems for decades, smart grid cybersecurity is an emerging field. To achieve security at all levels, operational technology-based security is also needed. To address this need, this paper develops an intrusion detection system for smart grids utilizing IEC 61850's Generic Object-Oriented Substation Event (GOOSE) messages. The system is developed with machine learning and is able to monitor the communication traffic of a given power system and distinguish normal events from abnormal ones, i.e., attacks. The designed system is implemented and tested with a realistic IEC 61850 GOOSE message dataset under symmetric and asymmetric fault conditions in the power system. The results show that the proposed system can successfully distinguish normal power system events from cyberattacks with high accuracy. This ensures that smart grids have intrusion detection in addition to cybersecurity features attached to exchanged messages.en_US
dc.description.sponsorshipMinistry of Energy, Transportation and Industry, METI, Japanen_US
dc.description.sponsorshipThis work was supported by the Ministry of Energy, Transportation and Industry, METI, Japan.en_US
dc.identifier.doi10.3390/sym13050826
dc.identifier.issn2073-8994
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85106583791en_US
dc.identifier.urihttps://doi.org/10.3390/sym13050826
dc.identifier.urihttps://hdl.handle.net/20.500.12452/15980
dc.identifier.volume13en_US
dc.identifier.wosWOS:000654610200001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSymmetry-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSmart Grid Cybersecurityen_US
dc.subjectGoose Message Securityen_US
dc.subjectIec 62351en_US
dc.subjectIntrusion Detectionen_US
dc.subjectArtificial Intelligenceen_US
dc.titleMachine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messagesen_US
dc.typeArticleen_US

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