A Network Intrusion Detection System: Enhanced Classification via Clustering Model

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dc.contributor.authorBalogun, Abdullateef Oluwagbemiga
dc.contributor.authorBalogun, Adedayo Miftaudeen
dc.contributor.authorAdeyemo, Victor Elijah
dc.contributor.authorSadiku, Peter Ogirima
dc.date.accessioned2018-05-23T10:33:59Z
dc.date.available2018-05-23T10:33:59Z
dc.date.issued2015-12-10
dc.description.abstractThe aim of developing an IDS is to build a system that oversee the general protection of a network from attacks both from withinand without, and doing so accurately. Optimization of IDS has also being receiving attention from the research community due toits large volumes of security audit data. In developing an IDS, most dataset used have high dimension in which only few attributes are needed for building an IDS – feature selection is used to solve this little problem. In this paper, we present and analyze the performance of some machine learning algorithm which performs classification via clustering using the KDDcup’99 dataset. Using the WEKA tool, simulations were ran and results was deduced after applying the proposed models to the dataset containing all the type of attacksen_US
dc.identifier.citation1. Balogun, A. O., Balogun, A. M., Adeyemo, V. E., & Sadiku, P. O. (2015): A Network Intrusion Detection System: Enhanced Classification via Clustering Model. Computing, Information System Development Informatics & Allied Research Journals. 6(4):53-58en_US
dc.identifier.issn2167-1710
dc.identifier.urihttps://static.secure.website/wscfus/8466857/3078478/v6n4p7-cisdiar-journal-a-network-intrusion-detection-system-enhanced-classification-via-clustering-model.pdf
dc.identifier.urihttp://hdl.handle.net/123456789/260
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), USAen_US
dc.relation.ispartofseriesVolume: 6;Issue: 4
dc.subjectMachine Learningen_US
dc.subjectData Miningen_US
dc.subjectNetwork securityen_US
dc.titleA Network Intrusion Detection System: Enhanced Classification via Clustering Modelen_US
dc.typeArticleen_US

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