Comparative study between fuzzy C-Means algorithm and artificial immune network algorithm in intrusion detection system

Intrusion Detection Systems (IDS) is special software developed in order to protect the system against security threats and malware. IDS provides second line of defense after rule based firewall. Unfortunately IDS with supervised learning approach heavily rely on labeled training data and generally...

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Main Author:	Abunada, Ahmed A. A.
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/33102/5/AhmedAAAbunadaMFSKSM2013.pdf
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id	my-utm-ep.33102
record_format	uketd_dc
spelling	my-utm-ep.331022017-07-24T07:46:42Z Comparative study between fuzzy C-Means algorithm and artificial immune network algorithm in intrusion detection system 2013-01 Abunada, Ahmed A. A. QA75 Electronic computers. Computer science Intrusion Detection Systems (IDS) is special software developed in order to protect the system against security threats and malware. IDS provides second line of defense after rule based firewall. Unfortunately IDS with supervised learning approach heavily rely on labeled training data and generally it fails to detect novel attacks and produces high false alarm. Besides, data labeling is expensive and time consuming. However, a systematic method which offers the capability to alleviate this problem is through the use of unsupervised approaches, which is the basis for this research. In addition to that, to investigate this phenomenon, a comparison between two clustering algorithms based on an anomaly detection system IDS is proposed. Related literature has given a direction towards comparing two clustering algorithm which are Artificial Immune Network (AIN) and Fuzzy c-means (FCM). The performance of those two clustering algorithm were measured based on false positive rate, false negative rate, hit rate and detection. This study has evaluated and analyzed AIN and FCM clustering algorithms. The finding shows that AIN gives higher overall accuracy and hit rate. It also gives lower false alarms on both datasets used in the study. Consistent good performances of AIN in clustering network traffic data into respective classes has made AIN a promising clustering technique to be of used in detection novel attack traffic in IDS. 2013-01 Thesis http://eprints.utm.my/id/eprint/33102/ http://eprints.utm.my/id/eprint/33102/5/AhmedAAAbunadaMFSKSM2013.pdf application/pdf en public 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 Abunada, Ahmed A. A. Comparative study between fuzzy C-Means algorithm and artificial immune network algorithm in intrusion detection system
description	Intrusion Detection Systems (IDS) is special software developed in order to protect the system against security threats and malware. IDS provides second line of defense after rule based firewall. Unfortunately IDS with supervised learning approach heavily rely on labeled training data and generally it fails to detect novel attacks and produces high false alarm. Besides, data labeling is expensive and time consuming. However, a systematic method which offers the capability to alleviate this problem is through the use of unsupervised approaches, which is the basis for this research. In addition to that, to investigate this phenomenon, a comparison between two clustering algorithms based on an anomaly detection system IDS is proposed. Related literature has given a direction towards comparing two clustering algorithm which are Artificial Immune Network (AIN) and Fuzzy c-means (FCM). The performance of those two clustering algorithm were measured based on false positive rate, false negative rate, hit rate and detection. This study has evaluated and analyzed AIN and FCM clustering algorithms. The finding shows that AIN gives higher overall accuracy and hit rate. It also gives lower false alarms on both datasets used in the study. Consistent good performances of AIN in clustering network traffic data into respective classes has made AIN a promising clustering technique to be of used in detection novel attack traffic in IDS.
format	Thesis
qualification_level	Master's degree
author	Abunada, Ahmed A. A.
author_facet	Abunada, Ahmed A. A.
author_sort	Abunada, Ahmed A. A.
title	Comparative study between fuzzy C-Means algorithm and artificial immune network algorithm in intrusion detection system
title_short	Comparative study between fuzzy C-Means algorithm and artificial immune network algorithm in intrusion detection system
title_full	Comparative study between fuzzy C-Means algorithm and artificial immune network algorithm in intrusion detection system
title_fullStr	Comparative study between fuzzy C-Means algorithm and artificial immune network algorithm in intrusion detection system
title_full_unstemmed	Comparative study between fuzzy C-Means algorithm and artificial immune network algorithm in intrusion detection system
title_sort	comparative study between fuzzy c-means algorithm and artificial immune network algorithm in intrusion detection system
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/33102/5/AhmedAAAbunadaMFSKSM2013.pdf
_version_	1747816079861743616

Comparative study between fuzzy C-Means algorithm and artificial immune network algorithm in intrusion detection system

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