Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques

vii Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool such as Intrusion Detection Syste...

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Main Authors:	Iftikhar , Ahmad, Azween, Abdullah
Format:	Thesis
Published:	2011
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utp.edu.my/5710/1/PhD-IT-Thesis-Iftikhar-Ahmad-G00941-session2009-2011.pdf
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spelling	my-utp-ep.57102012-12-31T04:18:43Z Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques 2011-02 Iftikhar , Ahmad Azween, Abdullah QA75 Electronic computers. Computer science vii Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), checkpoints and firewalls. Therefore, accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. Such weaknesses of the existing techniques have motivated the research presented in this thesis. One of the weaknesses of the existing intrusion detection approaches is the usage of a raw dataset for classification but the classifier may get confused due to redundancy and hence may not classify correctly. To overcome this issue, Principal Component Analysis (PCA) has been employed to transform raw features into principal features space and select the features based on their sensitivity. The sensitivity is determined by the values of eigenvalues. The recent approaches use PCA to project features space to principal feature space and select features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power. Based on the selected features, the classification is performed. The Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used for classification purpose due to their proven ability in classification. This research work uses the Knowledge Discovery and Data mining (KDD) cup dataset, which is considered benchmark for evaluating security detection mechanisms. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates. 2011-02 Thesis http://eprints.utp.edu.my/5710/ http://eprints.utp.edu.my/5710/1/PhD-IT-Thesis-Iftikhar-Ahmad-G00941-session2009-2011.pdf application/zip phd UNIVERSITI TEKNOLOGI PETRONAS CIS
institution	Universiti Teknologi PETRONAS
collection	UTP Institutional Repository
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Iftikhar , Ahmad Azween, Abdullah Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
description	vii Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), checkpoints and firewalls. Therefore, accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. Such weaknesses of the existing techniques have motivated the research presented in this thesis. One of the weaknesses of the existing intrusion detection approaches is the usage of a raw dataset for classification but the classifier may get confused due to redundancy and hence may not classify correctly. To overcome this issue, Principal Component Analysis (PCA) has been employed to transform raw features into principal features space and select the features based on their sensitivity. The sensitivity is determined by the values of eigenvalues. The recent approaches use PCA to project features space to principal feature space and select features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power. Based on the selected features, the classification is performed. The Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used for classification purpose due to their proven ability in classification. This research work uses the Knowledge Discovery and Data mining (KDD) cup dataset, which is considered benchmark for evaluating security detection mechanisms. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.
format	Thesis
qualification_level	Doctorate
author	Iftikhar , Ahmad Azween, Abdullah
author_facet	Iftikhar , Ahmad Azween, Abdullah
author_sort	Iftikhar , Ahmad
title	Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
title_short	Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
title_full	Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
title_fullStr	Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
title_full_unstemmed	Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
title_sort	feature subset selection in intrusion detection using soft computing techniques
granting_institution	UNIVERSITI TEKNOLOGI PETRONAS
granting_department	CIS
publishDate	2011
url	http://eprints.utp.edu.my/5710/1/PhD-IT-Thesis-Iftikhar-Ahmad-G00941-session2009-2011.pdf
_version_	1747837931936022528

Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques

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