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Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System

dc.contributor.editorOrjuela-Canon A.D.
dc.creatorÁlvarez Almeida L.A.
dc.creatorCarlos Martinez Santos J.
dc.date.accessioned2020-03-26T16:33:02Z
dc.date.available2020-03-26T16:33:02Z
dc.date.issued2019
dc.identifier.citation2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Proceedings
dc.identifier.isbn9781728116143
dc.identifier.urihttps://hdl.handle.net/20.500.12585/9137
dc.description.abstractThe integrity of information and services is one of the more evident concerns in the world of global information security, due to the fact that it has economic repercussions on the digital industry. For this reason, big companies spend a lot of money on systems that protect them against cyber-attacks like Denial of Service attacks. In this article, we will use all the attributes of the data-set NSL-KDD to train and test a Support Vector Machine model. This model will then be applied to a method of feature selection to obtain the most relevant attributes within the aforementioned data-set and train the model again. The main goal is comparing the results obtained in both instances of training and validate which was more efficient. © 2019 IEEE.eng
dc.description.sponsorshipEEE Colombia Section;EEE Colombian Caribbean Section;IEEE Computational Intelligence Colombian Chapter;IEEE Computational Intelligence Society
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85070855791&doi=10.1109%2fColCACI.2019.8781803&partnerID=40&md5=e5847944721efd67a906bd5aaabba5f9
dc.sourceScopus2-s2.0-85070855791
dc.titleEvaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System
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datacite.rightshttp://purl.org/coar/access_right/c_16ec
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94f
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.source.event2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1109/ColCACI.2019.8781803
dc.subject.keywordsClassification model
dc.subject.keywordsData set
dc.subject.keywordsDos Attacks
dc.subject.keywordsFeature selection
dc.subject.keywordsMachine learning
dc.subject.keywordsSupport vector machine
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsClassification (of information)
dc.subject.keywordsDenial-of-service attack
dc.subject.keywordsIntrusion detection
dc.subject.keywordsLearning systems
dc.subject.keywordsNetwork security
dc.subject.keywordsStatistical tests
dc.subject.keywordsSupport vector machines
dc.subject.keywordsClassification models
dc.subject.keywordsCyber-attacks
dc.subject.keywordsData set
dc.subject.keywordsFeatures selection
dc.subject.keywordsIntrusion Detection Systems
dc.subject.keywordsSupport vector machine models
dc.subject.keywordsFeature extraction
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.relation.conferencedate5 June 2019 through 7 June 2019
dc.type.spaConferencia
dc.identifier.orcid57210565161
dc.identifier.orcid26325154200


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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.