Show simple item record

dc.contributor.authorVega Vega, Rafael Alejandro
dc.contributor.authorChamoso, Pablo
dc.contributor.authorGonzález Briones, Alfonso
dc.contributor.authorCasteleiro-Roca, José-Luis
dc.contributor.authorJove, Esteban
dc.contributor.authorMeizoso-López, María del Carmen
dc.contributor.authorRodríguez-Gómez, Benigno Antonio
dc.contributor.authorQuintián, Héctor
dc.contributor.authorHerrero Cosío, Álvaro 
dc.contributor.authorMatsui, Kenji
dc.contributor.authorCorchado, Emilio 
dc.contributor.authorCalvo-Rolle, José Luis
dc.date.accessioned2023-01-13T13:51:06Z
dc.date.available2023-01-13T13:51:06Z
dc.date.issued2020-03
dc.identifier.urihttp://hdl.handle.net/10259/7243
dc.description.abstractThe present research work focuses on overcoming cybersecurity problems in the Smart Grid. Smart Grids must have feasible data capture and communications infrastructure to be able to manage the huge amounts of data coming from sensors. To ensure the proper operation of next-generation electricity grids, the captured data must be reliable and protected against vulnerabilities and possible attacks. The contribution of this paper to the state of the art lies in the identification of cyberattacks that produce anomalous behaviour in network management protocols. A novel neural projectionist technique (Beta Hebbian Learning, BHL) has been employed to get a general visual representation of the traffic of a network, making it possible to identify any abnormal behaviours and patterns, indicative of a cyberattack. This novel approach has been validated on 3 different datasets, demonstrating the ability of BHL to detect different types of attacks, more effectively than other state-of-the-art methods.en
dc.description.sponsorshipTEACHING STAFF MOBILITY UNDER BILATERAL AGREEMENTS (University of Salamanca—Osaka Institute of Technology 2019).en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofApplied sciences. 2020, V. 10, n. 7, e2276es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSmart griden
dc.subjectComputational intelligenceen
dc.subjectAutomatic responseen
dc.subjectExploratory projection pursuiten
dc.subjectNeural networken
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleIntrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Gridsen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/app10072276es
dc.identifier.doi10.3390/app10072276
dc.identifier.essn2076-3417
dc.journal.titleApplied Sciencesen
dc.volume.number10es
dc.issue.number7es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record