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dc.contributor.authorZugasti Uriguen, Ekhi
dc.contributor.authorGaritano Garitano, Iñaki
dc.contributor.authorIturbe Urretxa, Mikel
dc.contributor.authorZurutuza Ortega, Urko
dc.date.accessioned2019-04-03T10:04:51Z
dc.date.available2019-04-03T10:04:51Z
dc.date.issued2018
dc.identifier.isbn978-1538-66451-3en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=148614en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/1177
dc.description.abstractIndustrial environments have vastly changed sincethe conception of initial primitive and isolated networks. Thecurrent full interconnection paradigm, where connectivity be-tween different devices and the Internet has become a businessnecessity, has driven device interconnectivity towards buildingthe Industrial Internet of Things (IIoT), enabling added valueservices such as supply chain optimization or improved processcontrol. However, whereas interconnectivity has increased, IIoTsecurity practices has not evolved at the same pace, due partlyto inherited security practices from when industrial networkswhere not connected and the existence of basic hardware withno security functionalities. In this work, we present an AnomalyDetection System for industrial environments that monitorsphysical quantities to detect intrusions. It is based in the nullspace detection, which is at the same time, based on StochasticSubspace Identification (SSI). The approach is validated usingthe Tennessee-Eastman chemical process.en
dc.description.sponsorshipComisión Europeaes
dc.description.sponsorshipGobierno de Españaes
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksen
dc.subjectanomaly detectionen
dc.subjectIndustrial control systemsen
dc.subjectIndustrial internet of thingsen
dc.subjectNull spaceen
dc.titleNull is Not Always Empty: Monitoring the Null Space for Field-Level Anomaly Detection in Industrial IoT Environmentsen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.source2018 Global Internet of Things Summit, GIoTS.en
dc.description.versioninfo:eu-repo/semantics/acceptedVersionen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttp://dx.doi.org/10.1109/GIOTS.2018.8534574en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/737459/EU/Electronics and ICT as enabler for digital industry and optimized supply chain management covering the entire product lifecycle/PRODUCTIVE 4.0en
local.relation.projectIDGE/Acciones de programación conjunta internacional, del programa Estatal de investigación, desarrollo e innovación orientada a los retos de la sociedad, del plan estatal de investigación científica y técnica y de innovación 2013-2016, convocatoria 2017/PCIN-2017-071/ES/Electronica y TICs para facilitar la industria digital y optimizar la gestion de la cadena de Suministro cubriendo todo el ciclo de vida del producto/PRODUCTIVE 4.0es
local.relation.projectIDGV/Ikertalde Convocatoria 2016-2021/IT886-16/CAPV/Sistemas Inteligentes para Sistemas Industrialeses
local.source.detailsPp. 104-109. IEEE, 2018eu_ES


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