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dc.contributor.authorSedano, Javier
dc.contributor.authorGonzález González, Silvia .
dc.contributor.authorHerrero Cosío, Álvaro 
dc.contributor.authorBaruque Zanón, Bruno 
dc.contributor.authorCorchado, Emilio 
dc.date.accessioned2015-10-01T08:01:06Z
dc.date.available2015-10-01T08:01:06Z
dc.date.issued2012-09
dc.identifier.issn1367-0751
dc.identifier.urihttp://hdl.handle.net/10259/3860
dc.description.abstractAs it is well known, some Intrusion Detection Systems (IDSs) suffer from high rates of false positives and negatives. A mutation technique is proposed in this study to test and evaluate the performance of a full range of classifier ensembles for Network Intrusion Detection when trying to recognize new attacks. The novel technique applies mutant operators that randomly modify the features of the captured network packets to generate situations that could not otherwise be provided to IDSs while learning. A comprehensive comparison of supervised classifiers and their ensembles is performed to assess their generalization capability. It is based on the idea of confronting brand new network attacks obtained by means of the mutation technique. Finally, an example application of the proposed testing model is specially applied to the identification of network scans and related mutationsen
dc.description.sponsorshipSpanish Ministry of Science and Innovation (TIN2010-21272-C02-01 and CIT-020000-2009-12) (both funded by the European Regional Development Fund). The authors would also like to thank the vehicle interior manufacturer, Grupo Antolin Ingenieria S. A., within the framework of the MAGNO2008 - 1028.- CENIT. Project also funded by the MICINN, the Spanish Ministry of Science and Innovation (PID 560300-2009-11) and the Regional Government of Castile-Leon (CCTT/10/BU/0002). 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 Republic.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherOxford University Pressen
dc.relation.ispartofLogic Journal of the IGPL. 2012, V. 21, n. 4, p. 630-647en
dc.subjectNetwork intrusion detectionen
dc.subjectIDS performanceen
dc.subjectclassifier ensemblesen
dc.subjectmachine learningen
dc.subjectzero-day attacksen
dc.subjectmutationen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleMutating network scans for the assessment of supervised classifier ensemblesen
dc.typeinfo:eu-repo/semantics/article
dc.description.versionThis is a pre-copyedited, author-produced PDF of an article accepted for publication in Logic Journal of the IGPL following peer review. The version of record: Javier Sedano, Silvia González, Álvaro Herrero, Bruno Baruque, and Emilio Corchado, Mutating network scans for the assessment of supervised classifier ensembles, Logic Jnl IGPL, first published online September 3, 2012, doi:10.1093/jigpal/jzs037 is available online at: http://jigpal.oxfordjournals.org/content/early/2012/09/03/jigpal.jzs037
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttp://jigpal.oxfordjournals.org/content/early/2012/09/03/jigpal.jzs037
dc.identifier.doi10.1093/jigpal/jzs037
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/PID 560300-2009-11
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/PID 560300-2009-11
dc.relation.projectIDinfo:eu-repo/grantAgreement/JCyL/CCTT/10/BU/0002
dc.relation.projectIDinfo:eu-repo/grantAgreement/EU/OPRD/CZ.1.05/1.1.00/02.0070
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen


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