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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorIturbe, Mikel
dc.contributor.authorGaritano, Iñaki
dc.contributor.authorZurutuza, Urko
dc.contributor.authorUribeetxeberria, Roberto
dc.date.accessioned2022-10-31T10:38:40Z
dc.date.available2022-10-31T10:38:40Z
dc.date.issued2016
dc.identifier.isbn9789897581755en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=122681en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5789
dc.description.abstractIndustrial Control Systems are the set of specialized elements that monitor and control physical processes. Those systems are normally interconnected forming environments known as industrial networks. The particularities of these networks disallow the usage of traditional IT security mechanisms, while allowing other security strategies not suitable for IT networks. As industrial network traffic flows follow constant and repetitive patterns, whitelisting has been proved a viable approach for anomaly detection in industrial networks. In this paper, we present a network flow and related alert visualization system based on chord diagrams. The system represents the detected network flows within a time interval, highlighting the ones that do not comply the whitelisting rules. Moreover, it also depicts the network flows that, even if they are registered in the whitelist, have not been detected on the selected time interval (e.g. a host is down). Finally, the visualization system is tested w ith network data coming from a real industrial network.en
dc.language.isoengen
dc.publisherSCITEPRESSen
dc.rights© 2016 SCITEPRESSen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectIndustrial Networksen
dc.subjectSecurity Visualizationen
dc.subjectChord Diagramsen
dc.subjectFlow Monitoringen
dc.titleVisualizing Network Flows and Related Anomalies in Industrial Networks using Chord Diagrams and Whitelistingen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceProceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016)en
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.description.publicationfirstpage99en
local.description.publicationlastpage106en
local.identifier.doihttp://doi.org/10.5220/0005670000990106en
local.source.detailsVolume 2: IVAPP. Pp. 99-106. SCITEPRESS, Science and Technology Publications, 2016en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International