Intrusion detection system using hybrid GSA-k-Means

Security is an important aspect in our daily life. Intrusion Detection Systems (IDS) are developed to be the defense against security threats. Current signature based IDS like firewalls and antiviruses, which rely on labeled training data, generally cannot detect novel attacks. The purpose of this s...

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Main Author:	Aslahi, Bibi Masoomeh
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/78118/1/BibiMasoomehAslahiMFC20131.pdf
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spelling	my-utm-ep.781182018-07-25T08:17:37Z Intrusion detection system using hybrid GSA-k-Means 2013-01 Aslahi, Bibi Masoomeh QA75 Electronic computers. Computer science Security is an important aspect in our daily life. Intrusion Detection Systems (IDS) are developed to be the defense against security threats. Current signature based IDS like firewalls and antiviruses, which rely on labeled training data, generally cannot detect novel attacks. The purpose of this study is to improve the performance of IDS in terms of detection accuracy and reduce False Alarm Rate (FAR). Clustering is an important task in data mining that is used in IDS applications to detect novel attacks. Clustering refers to grouping together data objects so that objects within a cluster are similar to one another, while objects in different clusters are dissimilar. K-Means is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. The Gravitational Search Algorithm (GSA) is one effective method for searching problem space to find a near optimal solution. In this study, a hybrid approach based on GSA and k-Means (GSA-kMeans), which uses the advantages of both algorithms, is presented. The performance of GSA-kMeans is compared with other well-known algorithms, including k-Means and Gravitational Search Algorithm (GSA). Experimental results on the KDDCup 1999 dataset have demonstrated that the proposed method is more efficient in the detection of intrusive behavior than conventional k-Means and standard GSA which shows 80.62% detection accuracy and 7.45% FAR. 2013-01 Thesis http://eprints.utm.my/id/eprint/78118/ http://eprints.utm.my/id/eprint/78118/1/BibiMasoomehAslahiMFC20131.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:82652 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Aslahi, Bibi Masoomeh Intrusion detection system using hybrid GSA-k-Means
description	Security is an important aspect in our daily life. Intrusion Detection Systems (IDS) are developed to be the defense against security threats. Current signature based IDS like firewalls and antiviruses, which rely on labeled training data, generally cannot detect novel attacks. The purpose of this study is to improve the performance of IDS in terms of detection accuracy and reduce False Alarm Rate (FAR). Clustering is an important task in data mining that is used in IDS applications to detect novel attacks. Clustering refers to grouping together data objects so that objects within a cluster are similar to one another, while objects in different clusters are dissimilar. K-Means is a simple and efficient algorithm that is widely used for data clustering. However, its performance depends on the initial state of centroids and may trap in local optima. The Gravitational Search Algorithm (GSA) is one effective method for searching problem space to find a near optimal solution. In this study, a hybrid approach based on GSA and k-Means (GSA-kMeans), which uses the advantages of both algorithms, is presented. The performance of GSA-kMeans is compared with other well-known algorithms, including k-Means and Gravitational Search Algorithm (GSA). Experimental results on the KDDCup 1999 dataset have demonstrated that the proposed method is more efficient in the detection of intrusive behavior than conventional k-Means and standard GSA which shows 80.62% detection accuracy and 7.45% FAR.
format	Thesis
qualification_level	Master's degree
author	Aslahi, Bibi Masoomeh
author_facet	Aslahi, Bibi Masoomeh
author_sort	Aslahi, Bibi Masoomeh
title	Intrusion detection system using hybrid GSA-k-Means
title_short	Intrusion detection system using hybrid GSA-k-Means
title_full	Intrusion detection system using hybrid GSA-k-Means
title_fullStr	Intrusion detection system using hybrid GSA-k-Means
title_full_unstemmed	Intrusion detection system using hybrid GSA-k-Means
title_sort	intrusion detection system using hybrid gsa-k-means
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/78118/1/BibiMasoomehAslahiMFC20131.pdf
_version_	1747817911387422720

Intrusion detection system using hybrid GSA-k-Means

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