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

dc.contributorOrjuela-Canon A.D.
dc.creatorÁlvarez Almeida L.A.
dc.creatorCarlos Martinez Santos J.
dc.date.accessioned2020-03-26T16:33:02Z
dc.date.accessioned2022-09-28T20:23:06Z
dc.date.available2020-03-26T16:33:02Z
dc.date.available2022-09-28T20:23:06Z
dc.date.created2020-03-26T16:33:02Z
dc.date.issued2019
dc.identifier2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Proceedings
dc.identifier9781728116143
dc.identifierhttps://hdl.handle.net/20.500.12585/9137
dc.identifier10.1109/ColCACI.2019.8781803
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio UTB
dc.identifier57210565161
dc.identifier26325154200
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3726987
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.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation5 June 2019 through 7 June 2019
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rightsAtribución-NoComercial 4.0 Internacional
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.source2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019
dc.titleEvaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System


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