Orjuela-Canon A.D.2020-03-262020-03-2620192019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Proceedings9781728116143https://hdl.handle.net/20.500.12585/9137The 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.Recurso electrónicoapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObject10.1109/ColCACI.2019.8781803Classification modelData setDos AttacksFeature selectionMachine learningSupport vector machineArtificial intelligenceClassification (of information)Denial-of-service attackIntrusion detectionLearning systemsNetwork securityStatistical testsSupport vector machinesClassification modelsCyber-attacksData setFeatures selectionIntrusion Detection SystemsSupport vector machine modelsFeature extractioninfo:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 InternacionalUniversidad Tecnológica de BolívarRepositorio UTB5721056516126325154200