Influence of Feature Selection On Multi-Layer Perceptron Classifier for Intrusion Detection System

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dc.contributor.authorMabayoje, Modinat Abolore
dc.contributor.authorBalogun, Abdullateef Oluwagbemiga
dc.contributor.authorAmeen, Ahmed Oloduowo
dc.contributor.authorAdeyemo, Victor Elijah
dc.date.accessioned2018-05-23T10:34:48Z
dc.date.available2018-05-23T10:34:48Z
dc.date.issued2016-12-15
dc.description.abstractThe usage of the most popular neural network – Multilayer perceptron, as gained ground for the purpose of detecting intrusion. A lot of researchers had used it judiciously but there exist problem of slow training time and data over-fitting. This paper reviews the various data mining techniques for applied in the area intrusion detection, categories of attacks, and techniques for feature selection. This paper proposes an architecture where information gain is used for feature selection and multilayer perceptron (MLP) for classification on KDD’99 dataset. Evaluation of the performance of the MLP classifier on the KDD’99 dataset and also on the reduced dataset was conducted.en_US
dc.identifier.citationA. O. Balogun, A. M. Balogun, V. E. Adeyemo, P. O. Sadiku - A Network Intrusion Detection System: Enhanced Classification via Clustering Model. Computing, Information System Development Informatics & Allied Research Journals. 6(4):53-58. 2015.en_US
dc.identifier.issn2167-1710
dc.identifier.urihttp://docs.wixstatic.com/ugd/185b0a_357d646afcc64d34883dc3fa7ba0621e.pdf
dc.identifier.urihttp://hdl.handle.net/123456789/261
dc.language.isoenen_US
dc.publisherResearch Nexus Africa’s Networks in Conjunction with The African Institute of Development Informatics & Policy (AIDIP) Ghana & The International Centre for Information Technology & Development (ICITD), USA.en_US
dc.relation.ispartofseriesVolume: 7;Issue: 4
dc.subjectMachine Learningen_US
dc.subjectData Miningen_US
dc.subjectNetwork securityen_US
dc.subjectIntrusion Detectionen_US
dc.titleInfluence of Feature Selection On Multi-Layer Perceptron Classifier for Intrusion Detection Systemen_US
dc.typeArticleen_US

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