An Optimum-Path Forest framework for intrusion detection in computer networks

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dc.contributor.authorPereira, Clayton R. [UNESP]
dc.contributor.authorNakamura, Rodrigo Y. M. [UNESP]
dc.contributor.authorCosta, Kelton A. P. [UNESP]
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:25:56Z
dc.date.available2014-05-20T13:25:56Z
dc.date.issued2012-09-01
dc.description.abstractIntrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In order to overcome such limitations, we have introduced a new pattern recognition technique called optimum-path forest (OPF) to this task. Our proposal is composed of three main contributions: to apply OPF for intrusion detection, to identify redundancy in some public datasets and also to perform feature selection over them. The experiments have been carried out on three datasets aiming to compare OPF against Support Vector Machines, Self Organizing Maps and a Bayesian classifier. We have showed that OPF has been the fastest classifier and the always one with the top results. Thus, it can be a suitable tool to detect intrusions on computer networks, as well as to allow the algorithm to learn new attacks faster than other techniques. (C) 2012 Elsevier Ltd. All rights reserved.en
dc.description.affiliationUNESP Univ Estadual Paulista, Dept Comp, Bauru, Brazil
dc.description.affiliationUnespUNESP Univ Estadual Paulista, Dept Comp, Bauru, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 09/16206-1
dc.description.sponsorshipIdFAPESP: 10/02045-3
dc.description.sponsorshipIdFAPESP: 10/11676-7
dc.format.extent1226-1234
dc.identifierhttp://dx.doi.org/10.1016/j.engappai.2012.03.008
dc.identifier.citationEngineering Applications of Artificial Intelligence. Oxford: Pergamon-Elsevier B.V. Ltd, v. 25, n. 6, p. 1226-1234, 2012.
dc.identifier.doi10.1016/j.engappai.2012.03.008
dc.identifier.issn0952-1976
dc.identifier.lattes9039182932747194
dc.identifier.urihttp://hdl.handle.net/11449/8282
dc.identifier.wosWOS:000308122700012
dc.language.isoeng
dc.publisherPergamon-Elsevier B.V. Ltd
dc.relation.ispartofEngineering Applications of Artificial Intelligence
dc.relation.ispartofjcr2.819
dc.relation.ispartofsjr0,874
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectIntrusion detection systemen
dc.subjectOptimum-Path Foresten
dc.subjectComputer securityen
dc.subjectMachine learningen
dc.titleAn Optimum-Path Forest framework for intrusion detection in computer networksen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderPergamon-Elsevier B.V. Ltd
unesp.author.lattes9039182932747194
unesp.author.orcid0000-0001-5458-3908[3]
unesp.author.orcid0000-0002-6494-7514[4]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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