Ensemble methods in intrusion detection

As services are being deployed on the internet, there is the need to secure the infrastructure from malicious attacks. Intrusion detection serves as a second line of defense apart from firewall and cryptography. There are many techniques employed in intrusion detection which include signature detect...

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Main Author:	Josiah, Kekere Temitope
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/53615/25/KekereTemitopeJosiahMFC2015.pdf
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id	my-utm-ep.53615
record_format	uketd_dc
spelling	my-utm-ep.536152020-07-22T04:02:02Z Ensemble methods in intrusion detection 2015-01 Josiah, Kekere Temitope QA75 Electronic computers. Computer science As services are being deployed on the internet, there is the need to secure the infrastructure from malicious attacks. Intrusion detection serves as a second line of defense apart from firewall and cryptography. There are many techniques employed in intrusion detection which include signature detection, anomaly and specification based detection system. These techniques often trade off accuracy with false positive rate. In this study, anomaly detection using ensembles is used to automatically classify and detect attack patterns. It has been proven that ensembles of classifier outperform their base classifiers. Several multiples of classifiers have been combined to improve the performance of intrusion detection system. Commonly used classifiers include Support Vector Machines, Decision Trees, Genetic Algorithms, Fuzzy, Principal Component Analysis. The study employed KStar clustering and Instance Based classification algorithms to detect intrusions in NSL-KDD dataset. The results show that the ensemble we designed has a 1-error rate of 99.67% and false positive 0.33%. The response time of the anomaly is 0.18seconds. The chosen ensemble outperformed the rest of the ensembles (rPART & SMO and J48) and the base classifiers. The performance of the combiners has showed that the study has built a model with high detection, and reduced error. 2015-01 Thesis http://eprints.utm.my/id/eprint/53615/ http://eprints.utm.my/id/eprint/53615/25/KekereTemitopeJosiahMFC2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:84295 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 Josiah, Kekere Temitope Ensemble methods in intrusion detection
description	As services are being deployed on the internet, there is the need to secure the infrastructure from malicious attacks. Intrusion detection serves as a second line of defense apart from firewall and cryptography. There are many techniques employed in intrusion detection which include signature detection, anomaly and specification based detection system. These techniques often trade off accuracy with false positive rate. In this study, anomaly detection using ensembles is used to automatically classify and detect attack patterns. It has been proven that ensembles of classifier outperform their base classifiers. Several multiples of classifiers have been combined to improve the performance of intrusion detection system. Commonly used classifiers include Support Vector Machines, Decision Trees, Genetic Algorithms, Fuzzy, Principal Component Analysis. The study employed KStar clustering and Instance Based classification algorithms to detect intrusions in NSL-KDD dataset. The results show that the ensemble we designed has a 1-error rate of 99.67% and false positive 0.33%. The response time of the anomaly is 0.18seconds. The chosen ensemble outperformed the rest of the ensembles (rPART & SMO and J48) and the base classifiers. The performance of the combiners has showed that the study has built a model with high detection, and reduced error.
format	Thesis
qualification_level	Master's degree
author	Josiah, Kekere Temitope
author_facet	Josiah, Kekere Temitope
author_sort	Josiah, Kekere Temitope
title	Ensemble methods in intrusion detection
title_short	Ensemble methods in intrusion detection
title_full	Ensemble methods in intrusion detection
title_fullStr	Ensemble methods in intrusion detection
title_full_unstemmed	Ensemble methods in intrusion detection
title_sort	ensemble methods in intrusion detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2015
url	http://eprints.utm.my/id/eprint/53615/25/KekereTemitopeJosiahMFC2015.pdf
_version_	1747817595000586240

Ensemble methods in intrusion detection

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