Influence of Feature Selection On Multi-Layer Perceptron Classifier for Intrusion Detection System
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Date
2016-12-15
Journal Title
Journal ISSN
Volume Title
Publisher
Research 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.
Abstract
The 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.
Description
Keywords
Machine Learning, Data Mining, Network security, Intrusion Detection
Citation
A. 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.