Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges

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dc.contributor.authorKuchaƙ, Karelcs
dc.contributor.authorFujdiak, Radekcs
dc.coverage.issue3cs
dc.coverage.volume25cs
dc.date.accessioned2025-06-10T11:56:00Z
dc.date.available2025-06-10T11:56:00Z
dc.date.issued2024-12-12cs
dc.description.abstractIndustrial networks, due to communication convergence, face a growing exposure to cyber threats, necessitating the need to address a wider range of threats, alongside their detectability and classification. As critical components designed with a strong emphasis on availability, industrial networks require precise classification of anomalies, encompassing not just cyber anomalies but also operational and service disruptions. This paper provides an analysis of these anomalies, categorizing them into three groups based on their impact. The key contribution of this study lies in the strategic distribution of data sources across the Operational Technology (OT) network, facilitating the collection of relevant data for application in Machine Learning (ML) or Neural Network (NN) models. A comprehensive review of current anomaly processing techniques in industrial networks is presented, identifying significant research challenges to advance artificial intelligence methods for anomaly classification in OT environments. Additionally, this work examines common statistical methods for anomaly detection and offers a comparative analysis of prevalent ML and NN techniques.en
dc.formattextcs
dc.format.extent1-14cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE SENSORS JOURNAL. 2024, vol. 25, issue 3, p. 1-14.en
dc.identifier.doi10.1109/JSEN.2024.3512857cs
dc.identifier.issn1558-1748cs
dc.identifier.orcid0000-0002-5972-9037cs
dc.identifier.orcid0000-0002-8319-0633cs
dc.identifier.other193646cs
dc.identifier.researcheridABG-4089-2020cs
dc.identifier.scopus56610269000cs
dc.identifier.urihttps://hdl.handle.net/11012/251600
dc.language.isoencs
dc.publisherInstitute of Electrical and Electronics Engineers Inc.cs
dc.relation.ispartofIEEE SENSORS JOURNALcs
dc.relation.urihttps://ieeexplore.ieee.org/document/10797650cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1558-1748/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectAnomaly typesen
dc.subjectsensory dataen
dc.subjectcyber-securityen
dc.subjectindustrial control system (ICS)en
dc.subjectoperational technology (OT)en
dc.subjectNeural Network (NN)en
dc.titleAnomaly Detection in Industrial Networks: Current State, Classification, and Key Challengesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.grantNumberinfo:eu-repo/grantAgreement/MSM/EH/EH22_008/0004617cs
sync.item.dbidVAV-193646en
sync.item.dbtypeVAVen
sync.item.insts2025.06.10 13:56:00en
sync.item.modts2025.06.10 13:33:06en
thesis.grantorVysokĂ© učenĂ­ technickĂ© v Brně. Fakulta elektrotechniky a komunikačnĂ­ch technologiĂ­. Ústav telekomunikacĂ­cs
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