eprintid: 1417
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/14/17
datestamp: 2023-11-09 15:49:34
lastmod: 2023-11-09 15:49:34
status_changed: 2023-11-09 15:40:34
type: article
metadata_visibility: show
creators_name: Ahmad, I.
creators_name: Abdullah, A.
creators_name: Alghamdi, A.
creators_name: Alnfajan, K.
creators_name: Hussain, M.
title: Intrusion detection using feature subset selection based on MLP
ispublished: pub
note: cited By 25
abstract: Intrusions are serious questions in network systems. Numerous intrusion detection techniques are present to tackle these problems but the dilemma is performance. To raise performance, it is momentous to raise the detection rates and decrease false alarm rates. The contemporary methods use Principal Component Analysis (PCA) to project features space to principal feature space and choose features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the best possible sensitivity for the classifier due to ignoring several sensitive features. Therefore, we applied a Genetic Algorithm (GA) to search the principal feature space for a subset of features with optimal sensitivity. So, in this research, a method for optimal features subset selection is proposed to overcome performance issues using PCA, GA and Multilayer Perceptron (MLP). The KDD-cup dataset is used. This method is capable to minimize amount of features and maximize the detection rates. © 2011 Academic Journals.
date: 2011
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855890718&doi=10.5897%2fSRE11.142&partnerID=40&md5=f0e8435070acb884f93ad1ff88c5d39a
id_number: 10.5897/SRE11.142
full_text_status: none
publication: Scientific Research and Essays
volume: 6
number: 34
pagerange: 6804-6810
refereed: TRUE
issn: 19922248
citation:   Ahmad, I. and Abdullah, A. and Alghamdi, A. and Alnfajan, K. and Hussain, M.  (2011) Intrusion detection using feature subset selection based on MLP.  Scientific Research and Essays, 6 (34).  pp. 6804-6810.  ISSN 19922248