Identification of industrial devices based on payload

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dc.contributor.authorPospíšil, Ondřejcs
dc.contributor.authorFujdiak, Radekcs
dc.date.accessioned2025-02-27T17:24:13Z
dc.date.available2025-02-27T17:24:13Z
dc.date.issued2024-07-30cs
dc.description.abstractIdentification of industrial devices based on their behavior in network communication is important from a cybersecurity perspective in two areas: attack prevention and digital forensics. In both areas, device identification falls under asset management or asset tracking. Due to the impact of active scanning on these networks, particularly in terms of latency, it is important to use passive scanning in industrial networks. For passive identification, statistical learning algorithms are nowadays the most appropriate. The aim of this paper is to demonstrate the potential for passive identification of PLC devices using statistical learning based on network communication, specifically the payload of the packet. Individual statistical parameters from 15 minutes of traffic based on payload entropy were used to create the features. Three scenarios were performed and the XGBoost algorithm was used for evaluation. In the best scenario, the model achieved an accuracy score of 83% to identify individual devices.en
dc.formattextcs
dc.format.extent1-9cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security. 2024, p. 1-9.en
dc.identifier.doi10.1145/3664476.3670462cs
dc.identifier.isbn979-8-4007-1718-5cs
dc.identifier.orcid0000-0002-8347-4847cs
dc.identifier.orcid0000-0002-8319-0633cs
dc.identifier.other189222cs
dc.identifier.scopus56610269000cs
dc.identifier.urihttps://hdl.handle.net/11012/250065
dc.language.isoencs
dc.publisherAssociation for Computing Machinerycs
dc.relation.ispartofARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Securitycs
dc.relation.urihttps://dl.acm.org/doi/10.1145/3664476.3670462cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectPLCen
dc.subjectOTen
dc.subjectIdentificationen
dc.subjectICSen
dc.subjectMLen
dc.subjectXGBoosten
dc.titleIdentification of industrial devices based on payloaden
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.grantNumberinfo:eu-repo/grantAgreement/TA0/FW/FW06010490cs
sync.item.dbidVAV-189222en
sync.item.dbtypeVAVen
sync.item.insts2025.02.27 18:24:12en
sync.item.modts2025.02.21 11:31:56en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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