Gain Ratio and Decision Tree Classifier for Intrusion Detection

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dc.contributor.authorMabayoje, Modinat Abolore
dc.contributor.authorAkintola, Abimbola Ganiyat
dc.contributor.authorBalogun, Abdullateef Oluwagbemiga
dc.contributor.authorAyilara, Opeyemi
dc.date.accessioned2018-05-10T13:35:19Z
dc.date.available2018-05-10T13:35:19Z
dc.date.issued2015
dc.description.abstractWith the evident need for accuracy in the performance of intrusion detection system, it is expedient that in addition to the algorithms used, more activities should be carried out to improve accuracy and reduce real time used in detection. This paper reviews how data mining relates to IDS, feature selection and classification. This paper proposes architecture of IDS where GainRatio is used for feature selection and decision tree for classification using NSL-KDD99 dataset, It also includes the evaluation of the performance of the Decision tree on the dataset and also on the reduced dataset.en_US
dc.identifier.citation18. Mabayoje M.A., Akintola, A. G., Balogun, A. O & Ayilara, O. (2015): Gain Ratio and Decision Tree Classifier for Intrusion Detection. International Journal of Computer Applications (IJCA). 126(1):56-59en_US
dc.identifier.issn0123-4560
dc.identifier.other10.5120/ijca2015905983
dc.identifier.otherhttps://www.ijcaonline.org/research/volume126/number1/modinat-2015-ijca-905983.pdf
dc.identifier.urihttp://hdl.handle.net/123456789/224
dc.language.isoenen_US
dc.publisherFoundation of Computer Science (FCS), NY, USAen_US
dc.relation.ispartofseriesVolume: 126;Issue: 1
dc.subjectMachine Learningen_US
dc.subjectData Miningen_US
dc.subjectKnowledge Discoveryen_US
dc.subjectIntrusion Detection Systemen_US
dc.titleGain Ratio and Decision Tree Classifier for Intrusion Detectionen_US
dc.typeArticleen_US

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