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dc.rights.licenseAttribution 4.0 International
dc.contributor.authorZurutuza, Urko
dc.contributor.otherSáez-de-Cámara, Xabier
dc.contributor.otherFlores, José Luis
dc.contributor.otherArellano, Cristóbal
dc.contributor.otherUrbieta, Aitor
dc.date.accessioned2024-02-02T08:53:12Z
dc.date.available2024-02-02T08:53:12Z
dc.date.issued2023
dc.identifier.issn0167-4048 (papel)
dc.identifier.issn1872-6208 (online)
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=173003
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6217
dc.descriptionAPC (formulario). 2790 EUR
dc.description.abstractThere is a growing trend of cyberattacks against Internet of Things (IoT) devices; moreover, the sophistication and motivation of those attacks is increasing. The vast scale of IoT, diverse hardware and software, and being typically placed in uncontrolled environments make traditional IT security mechanisms such as signature-based intrusion detection and prevention systems challenging to integrate. They also struggle to cope with the rapidly evolving IoT threat landscape due to long delays between the analysis and publication of the detection rules. Machine learning methods have shown faster response to emerging threats; however, model training architectures like cloud or edge computing face multiple drawbacks in IoT settings, including network overhead and data isolation arising from the large scale and heterogeneity that characterizes these networks.
dc.language.isoeng
dc.publisherElsevier
dc.rights© 2023 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAnomaly detection
dc.subjectBotnet
dc.subjectInternet of things
dc.subjectIntrusion detection
dc.subjectMachine learning
dc.subjectNetwork security
dc.titleClustered federated learning architecture for network anomaly detection in large scale heterogeneous IoT networks
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2
dcterms.sourceComputers and Security
local.contributor.groupAnálisis de datos y ciberseguridad
local.description.peerreviewedtrue
local.identifier.doihttps://doi.org/10.1016/j.cose.2023.103299
local.rights.publicationfeeAPC
local.rights.publicationfeeamount2790 EUR
local.contributor.otherinstitutionhttps://ror.org/03hp1m080
local.source.detailsVol. 131. N. art. 103299
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
oaire.funderNameEuropean Commission
oaire.funderNameEusko Jaurlaritza = Gobierno Vasco
oaire.funderIdentifierhttps://ror.org/00k4n6c32 http://data.crossref.org/fundingdata/funder/10.13039/501100000780
oaire.funderIdentifierhttps://ror.org/00pz2fp31 http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.fundingStreamH2020
oaire.fundingStreamElkartek 2023
oaire.awardNumber101021911
oaire.awardNumberKK-2023-00085
oaire.awardTitleA Cognitive Detection System for Cybersecure Operational (IDUNN)
oaire.awardTitlecyBErsecure industriAl Computing cONtinuum (BEACON)
oaire.awardURIhttps://doi.org/10.3030/101021911
oaire.awardURISin información


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