A hybrid methodology for anomaly detection in Cyber–Physical Systems

dc.contributor.author	Jeffrey, N.
dc.contributor.author	Tan, Q.
dc.contributor.author	Villar Flecha, José Ramón
dc.date.accessioned	2024-02-05T10:49:00Z
dc.date.available	2024-02-05T10:49:00Z
dc.date.issued	2024
dc.identifier.citation	Neurocomputing, 568 (2024); doi:10.1016/j.neucom.2023.127068
dc.identifier.issn	0925-2312
dc.identifier.uri	https://hdl.handle.net/10651/71154
dc.description.sponsorship	This 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.iso	eng	spa
dc.relation.ispartof	Neurocomputing. 568	spa
dc.rights	Atribución 4.0 Internacional	*
dc.rights	© 2023 The Author(s). Published by Elsevier B.V.
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.title	A hybrid methodology for anomaly detection in Cyber–Physical Systems	spa
dc.type	journal article	spa
dc.identifier.doi	10.1016/j.neucom.2023.127068
dc.relation.projectID	MINECO-TIN2017-84804-R	spa
dc.relation.projectID	info: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.projectID	RED2018-102312-T	spa
dc.relation.publisherversion	https://doi.org/10.1016/j.neucom.2023.127068	spa
dc.rights.accessRights	open access	spa
dc.type.hasVersion	VoR	spa

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