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A hybrid methodology for anomaly detection in Cyber–Physical Systems

dc.contributor.authorJeffrey, N.
dc.contributor.authorTan, Q.
dc.contributor.authorVillar Flecha, José Ramón 
dc.date.accessioned2024-02-05T10:49:00Z
dc.date.available2024-02-05T10:49:00Z
dc.date.issued2024
dc.identifier.citationNeurocomputing, 568 (2024); doi:10.1016/j.neucom.2023.127068
dc.identifier.issn0925-2312
dc.identifier.urihttps://hdl.handle.net/10651/71154
dc.description.sponsorshipThis research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R, PID2020-112726RB-I00 and the State Research Agency (AEI, Spain) under grant agreement No RED2018-102312-T (IA-Biomed).spa
dc.language.isoengspa
dc.relation.ispartofNeurocomputing. 568spa
dc.rightsAtribución 4.0 Internacional*
dc.rights© 2023 The Author(s). Published by Elsevier B.V.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleA hybrid methodology for anomaly detection in Cyber–Physical Systemsspa
dc.typejournal articlespa
dc.identifier.doi10.1016/j.neucom.2023.127068
dc.local.notesOA ATUO23
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84804-R/ES/INTELIGENCIA COMPUTACIONAL EN SITUACIONES DE ALTA INCERTIDUMBRE. APLICACIONES A TECNOLOGIAS ECOEFICIENTES/spa
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-112726RB-I00/ES/INTELIGENCIA COMPUTACIONAL PARA LA MITIGACION DE EMISIONES: NUEVAS METODOLOGIAS DE APRENDIZAJE CON DATOS INCOMPLETOS/spa
dc.relation.projectIDRED2018-102312-Tspa
dc.relation.publisherversionhttps://doi.org/10.1016/j.neucom.2023.127068spa
dc.rights.accessRightsopen accessspa
dc.type.hasVersionVoRspa


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