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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorIturbe, Mikel
dc.contributor.authorGaritano, Iñaki
dc.contributor.authorZurutuza, Urko
dc.contributor.authorUribeetxeberria, Roberto
dc.date.accessioned2018-10-25T14:16:09Z
dc.date.available2018-10-25T14:16:09Z
dc.date.issued2017
dc.identifier.issn1939-0122eu_ES
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=128631eu_ES
dc.identifier.urihttps://hdl.handle.net/20.500.11984/1111
dc.description.abstractIndustrial Networks (INs) are widespread environments where heterogeneous devices collaborate to control and monitor physical processes. Some of the controlled processes belong to Critical Infrastructures (CIs), and, as such, IN protection is an active research field. Among different types of security solutions, IN Anomaly Detection Systems (ADSs) have received wide attention from the scientific community.While INs have grown in size and in complexity, requiring the development of novel, Big Data solutions for data processing, IN ADSs have not evolved at the same pace. In parallel, the development of BigData frameworks such asHadoop or Spark has led the way for applying Big Data Analytics to the field of cyber-security,mainly focusing on the Information Technology (IT) domain. However, due to the particularities of INs, it is not feasible to directly apply IT security mechanisms in INs, as IN ADSs face unique characteristics. In this work we introduce three main contributions. First, we survey the area of Big Data ADSs that could be applicable to INs and compare the surveyed works. Second, we develop a novel taxonomy to classify existing INbased ADSs. And, finally, we present a discussion of open problems in the field of Big Data ADSs for INs that can lead to further development.eu_ES
dc.description.sponsorshipThis work has been developed by the Intelligent Systems for Industrial Systems group supported by the Department of Education, Language Policy and Culture of the Basque Government. This work has been partially funded by the European Union’s Horizon 2020 research and innovation programme project PROPHESY, under Grant Agreement no. 766994.eu_ES
dc.language.isoengeu_ES
dc.publisherThe Wiley Hindawi Partnershipeu_ES
dc.rights© 2017 Mikel Iturbe et al.eu_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleTowards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trendseu_ES
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2eu_ES
dcterms.sourceSecurity and Communication Networkseu_ES
local.contributor.groupAnálisis de datos y ciberseguridadeu_ES
local.description.peerreviewedtrueeu_ES
local.identifier.doihttps://doi.org/10.1155/2017/9150965eu_ES
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/766994/EU/Platform for rapid deployment of self-configuring and optimized predictive maintenance services/PROPHESYeu_ES
local.relation.projectIDGV/Grupos SISI/211030090/CAPV/Sistemas Inteligentes para Sistemas Industriales
local.rights.publicationfeeAPC
local.rights.publicationfeeamount1810 EUR
local.rights.publicationfeeamount
local.source.detailsVol. 2017. Article ID 9150965. November, 2017eu_ES
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501eu_ES
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85eu_ES


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