Mabayoje, Modinat AboloreBalogun, Abdullateef OluwagbemigaAmeen, Ahmed OloduowoAdeyemo, Victor Elijah2018-05-232018-05-232016-12-15A. 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. 2015.2167-1710http://docs.wixstatic.com/ugd/185b0a_357d646afcc64d34883dc3fa7ba0621e.pdfhttp://hdl.handle.net/123456789/261The 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.enMachine LearningData MiningNetwork securityIntrusion DetectionArticle