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dc.contributor.authorHerrero, Alvaro 
dc.contributor.authorZurutuza, Urko
dc.contributor.authorCorchado, Emilio 
dc.date.accessioned2015-10-01T09:34:40Z
dc.date.available2015-10-01T09:34:40Z
dc.date.issued2012-04
dc.identifier.issn0129-0657
dc.identifier.urihttp://hdl.handle.net/10259/3861
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 analyzeden
dc.description.sponsorshipRegional Government of Gipuzkoa, the Department of Research, Education and Universities of the Basque Government, and the Spanish Ministry of Science and Innovation (MICINN) under projects TIN2010-21272-C02-01 and CIT-020000-2009-12 (funded by the European Regional Development Fund). This work was also supported in the framework of the IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 supported by the Operational Program 'Research and Development for Innovations' funded through the Structural Funds of the European Union and the state budget of the Czech Republicen
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherWorld Scientific Publishingen
dc.relation.ispartofInternational Journal of Neural Systems. 2012, V. 22, n. 2, 1250005en
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.typeArtículoes
dc.typeinfo:eu-repo/semantics/article
dc.description.versionElectronic version of an article published as International Journal of Neural Systems, Volume 22, Issue 02, April 2012 10.1142/S0129065712500050 ©copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijns
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttp://www.worldscientific.com/doi/abs/10.1142/S0129065712500050
dc.identifier.doi10.1142/S0129065712500050
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/TIN2010-21272-C02-01
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/CIT-020000-2009-12
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/UE/RDI/CZ.1.05/1.1.00/02.0070
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen


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