Hybrid intelligent approach for network intrusion detection

In recent years, computer networks are broadly used, and they have become very complicated. A lot of sensitive information passes through various kinds of computer devices, ranging from minicomputers to servers and mobile devices. These occurring changes have led to draw the conclusion that the numb...

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Main Author:	Al-Mohammed, Wael Hasan Ali
Format:	Thesis
Language:	eng eng
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	https://etd.uum.edu.my/4520/1/s814522.pdf https://etd.uum.edu.my/4520/2/s814522_abstract.pdf
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id	my-uum-etd.4520
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mohammad Tahir, Hatim
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Al-Mohammed, Wael Hasan Ali Hybrid intelligent approach for network intrusion detection
description	In recent years, computer networks are broadly used, and they have become very complicated. A lot of sensitive information passes through various kinds of computer devices, ranging from minicomputers to servers and mobile devices. These occurring changes have led to draw the conclusion that the number of attacks on important information over the network systems is increasing with every year. Intrusion is the main threat to the network. It is defined as a series of activities aimed for exposing the security of network systems in terms of confidentiality, integrity and availability, as a result; intrusion detection is extremely important as a part of the defense. Hence, there must be substantial improvement in network intrusion detection techniques and systems. Due to the prevailing limitations of finding novel attacks, high false detection, and accuracy in previous intrusion detection approaches, this study has proposed a hybrid intelligent approach for network intrusion detection based on k-means clustering algorithm and support vector machine classification algorithm. The aim of this study is to reduce the rate of false alarm and also to improve the detection rate, comparing with the existing intrusion detection approaches. In the present study, NSL-KDD intrusion dataset has been used for training and testing the proposed approach. In order to improve classification performance, some steps have been taken beforehand. The first one is about unifying the types and filtering the dataset by data transformation. Then, a features selection algorithm is applied to remove irrelevant and noisy features for the purpose of intrusion. Feature selection has decreased the features from 41 to 21 features for intrusion detection and later normalization method is employed to perform and reduce the differences among the data. Clustering is the last step of processing before classification has been performed, using k-means algorithm. Under the purpose of classification, support vector machine have been used. After training and testing the proposed hybrid intelligent approach, the results of performance evaluation have shown that the proposed network intrusion detection has achieved high accuracy and low false detection rate. The accuracy is 96.025 percent and the false alarm is 3.715 percent.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Al-Mohammed, Wael Hasan Ali
author_facet	Al-Mohammed, Wael Hasan Ali
author_sort	Al-Mohammed, Wael Hasan Ali
title	Hybrid intelligent approach for network intrusion detection
title_short	Hybrid intelligent approach for network intrusion detection
title_full	Hybrid intelligent approach for network intrusion detection
title_fullStr	Hybrid intelligent approach for network intrusion detection
title_full_unstemmed	Hybrid intelligent approach for network intrusion detection
title_sort	hybrid intelligent approach for network intrusion detection
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2015
url	https://etd.uum.edu.my/4520/1/s814522.pdf https://etd.uum.edu.my/4520/2/s814522_abstract.pdf
_version_	1747827751567491072
spelling	my-uum-etd.45202021-03-18T03:30:20Z Hybrid intelligent approach for network intrusion detection 2015 Al-Mohammed, Wael Hasan Ali Mohammad Tahir, Hatim Awang Had Salleh Graduate School of Arts & Sciences College of Arts and Sciences QA75 Electronic computers. Computer science In recent years, computer networks are broadly used, and they have become very complicated. A lot of sensitive information passes through various kinds of computer devices, ranging from minicomputers to servers and mobile devices. These occurring changes have led to draw the conclusion that the number of attacks on important information over the network systems is increasing with every year. Intrusion is the main threat to the network. It is defined as a series of activities aimed for exposing the security of network systems in terms of confidentiality, integrity and availability, as a result; intrusion detection is extremely important as a part of the defense. Hence, there must be substantial improvement in network intrusion detection techniques and systems. Due to the prevailing limitations of finding novel attacks, high false detection, and accuracy in previous intrusion detection approaches, this study has proposed a hybrid intelligent approach for network intrusion detection based on k-means clustering algorithm and support vector machine classification algorithm. The aim of this study is to reduce the rate of false alarm and also to improve the detection rate, comparing with the existing intrusion detection approaches. In the present study, NSL-KDD intrusion dataset has been used for training and testing the proposed approach. In order to improve classification performance, some steps have been taken beforehand. The first one is about unifying the types and filtering the dataset by data transformation. Then, a features selection algorithm is applied to remove irrelevant and noisy features for the purpose of intrusion. Feature selection has decreased the features from 41 to 21 features for intrusion detection and later normalization method is employed to perform and reduce the differences among the data. Clustering is the last step of processing before classification has been performed, using k-means algorithm. Under the purpose of classification, support vector machine have been used. After training and testing the proposed hybrid intelligent approach, the results of performance evaluation have shown that the proposed network intrusion detection has achieved high accuracy and low false detection rate. The accuracy is 96.025 percent and the false alarm is 3.715 percent. 2015 Thesis https://etd.uum.edu.my/4520/ https://etd.uum.edu.my/4520/1/s814522.pdf text eng public https://etd.uum.edu.my/4520/2/s814522_abstract.pdf text eng public masters masters Universiti Utara Malaysia Ahmad, A., Bharanidharan Shanmugam, Norbik Bashah Idris, Ganthan Nayarana Samy, & AlBakri, S. H. (2013). 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Hybrid intelligent approach for network intrusion detection

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