Anomaly intrusion detection system using immune network with reduced network traffic features

Intrusion Detection Systems (IDS) are developed to be the defense against these security threats. Current signature based IDS like firewalls and anti viruses, which rely on labeled training data, generally can not detect novel attacks. A method that offers a promise to solve this problem is the anom...

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Main Author:	Qasem, Murad Abdo Rassam
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11367/6/MuradAbdoRassamMFSKSM2010.pdf
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spelling	my-utm-ep.113672017-09-28T03:21:18Z Anomaly intrusion detection system using immune network with reduced network traffic features 2010-04 Qasem, Murad Abdo Rassam QA75 Electronic computers. Computer science Intrusion Detection Systems (IDS) are developed to be the defense against these security threats. Current signature based IDS like firewalls and anti viruses, which rely on labeled training data, generally can not detect novel attacks. A method that offers a promise to solve this problem is the anomaly based IDS. Literature has shown that direction towards reducing false positive rate and thus enhancing the detection rate and speed have shifted from accurate machine learning classifiers to the adaptive models like bio-inspired models. Consequently, this study has been introduced to enhance the detection rate and speed up the detection process by reducing the network traffic features. Moreover, it aimed to investigate the implementation of the bio-inspired Immune Network approach for clustering different kinds of attacks. This approach aimed at enhancing the detection rate of novel attacks and thus decreasing the high false positive rate in IDS. Rough Set method was applied to reduce the dimension of KDD CUP ’99 dataset which used by this study and select only the features that best represent all kinds of attacks. Immune Network clustering was then applied using aiNet algorithm in order to cluster normal data from attacks in the testing dataset. The results revealed that detection rate and speed were enhanced by using only the most significant features. Furthermore, it was found that Immune Network clustering method is robust in detecting novel attacks in the test dataset. The principal conclusion was that IDS is enhanced by the use of significant network traffic features besides the implementation of the Immune Network clustering to detect novel attacks. 2010-04 Thesis http://eprints.utm.my/id/eprint/11367/ http://eprints.utm.my/id/eprint/11367/6/MuradAbdoRassamMFSKSM2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Qasem, Murad Abdo Rassam Anomaly intrusion detection system using immune network with reduced network traffic features
description	Intrusion Detection Systems (IDS) are developed to be the defense against these security threats. Current signature based IDS like firewalls and anti viruses, which rely on labeled training data, generally can not detect novel attacks. A method that offers a promise to solve this problem is the anomaly based IDS. Literature has shown that direction towards reducing false positive rate and thus enhancing the detection rate and speed have shifted from accurate machine learning classifiers to the adaptive models like bio-inspired models. Consequently, this study has been introduced to enhance the detection rate and speed up the detection process by reducing the network traffic features. Moreover, it aimed to investigate the implementation of the bio-inspired Immune Network approach for clustering different kinds of attacks. This approach aimed at enhancing the detection rate of novel attacks and thus decreasing the high false positive rate in IDS. Rough Set method was applied to reduce the dimension of KDD CUP ’99 dataset which used by this study and select only the features that best represent all kinds of attacks. Immune Network clustering was then applied using aiNet algorithm in order to cluster normal data from attacks in the testing dataset. The results revealed that detection rate and speed were enhanced by using only the most significant features. Furthermore, it was found that Immune Network clustering method is robust in detecting novel attacks in the test dataset. The principal conclusion was that IDS is enhanced by the use of significant network traffic features besides the implementation of the Immune Network clustering to detect novel attacks.
format	Thesis
qualification_level	Master's degree
author	Qasem, Murad Abdo Rassam
author_facet	Qasem, Murad Abdo Rassam
author_sort	Qasem, Murad Abdo Rassam
title	Anomaly intrusion detection system using immune network with reduced network traffic features
title_short	Anomaly intrusion detection system using immune network with reduced network traffic features
title_full	Anomaly intrusion detection system using immune network with reduced network traffic features
title_fullStr	Anomaly intrusion detection system using immune network with reduced network traffic features
title_full_unstemmed	Anomaly intrusion detection system using immune network with reduced network traffic features
title_sort	anomaly intrusion detection system using immune network with reduced network traffic features
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/11367/6/MuradAbdoRassamMFSKSM2010.pdf
_version_	1747814845097443328

Anomaly intrusion detection system using immune network with reduced network traffic features

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