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dc.contributor.authorZurutuza, Urko
dc.contributor.otherHerrero, Álvaro
dc.contributor.otherCorchado, Emilio
dc.date.accessioned2022-05-24T13:45:34Z
dc.date.available2022-05-24T13:45:34Z
dc.date.issued2012
dc.identifier.issn0129-0657en
dc.identifier.urihttp://hdl.handle.net/20.500.11984/5586
dc.description.abstractNeural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed.en
dc.language.isoengen
dc.publisherWorld Scientificen
dc.rights© 2012 World Scientificen
dc.subjectArtificial Neural Networksen
dc.subjectUnsupervised Learningen
dc.subjectProjection Modelsen
dc.subjectNetwork & Computer Securityen
dc.subjectIntrusion Detectionen
dc.subjectHoneypotsen
dc.titleA neural-visualization IDS for honeynet dataen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/openAccessen
dcterms.sourceInternational Journal of Neural Systemsen
dc.description.versioninfo:eu-repo/semantics/submittedVersionen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.description.publicationfirstpage121en
local.description.publicationlastpage128en
local.identifier.doihttps://doi.org/10.1142/S0129065712500050en
local.source.detailsVol. 22. Nº. 2. Pp 121-128, 2012en


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