A Statistical Approach Towards Worm Detection Using Cross-Relation Technique

Computer networks have become an important dimension of modern organizations. Thus, ensuring that networks run at peak performance (network utilization and speed running normal without any faults) is considered a crucial step for these organizations. To achieve this goal, networks must be secure bec...

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Bibliographic Details
Main Author:	Anbar, Mohammed F.R.
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/43403/1/Mohammed%20F.R.%20Anbar24.pdf
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id	my-usm-ep.43403
record_format	uketd_dc
spelling	my-usm-ep.434032019-04-12T05:26:17Z A Statistical Approach Towards Worm Detection Using Cross-Relation Technique 2013-03 Anbar, Mohammed F.R. QA75.5-76.95 Electronic computers. Computer science Computer networks have become an important dimension of modern organizations. Thus, ensuring that networks run at peak performance (network utilization and speed running normal without any faults) is considered a crucial step for these organizations. To achieve this goal, networks must be secure because security is one of the essential issues for reaching a good performance level (no faults in the network such as high rate of connection failure). However, this task is next to impossible especially when there are other issues that need to be addressed. This thesis focuses on detecting the presence of network worms in network, which is one of the most challenging problems in network security. By detecting the presence of network worms in the network, resources and services can be further protected by patching or installing security measures, such as firewalls, intrusion detection systems, or alternative computer systems. 2013-03 Thesis http://eprints.usm.my/43403/ http://eprints.usm.my/43403/1/Mohammed%20F.R.%20Anbar24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Anbar, Mohammed F.R. A Statistical Approach Towards Worm Detection Using Cross-Relation Technique
description	Computer networks have become an important dimension of modern organizations. Thus, ensuring that networks run at peak performance (network utilization and speed running normal without any faults) is considered a crucial step for these organizations. To achieve this goal, networks must be secure because security is one of the essential issues for reaching a good performance level (no faults in the network such as high rate of connection failure). However, this task is next to impossible especially when there are other issues that need to be addressed. This thesis focuses on detecting the presence of network worms in network, which is one of the most challenging problems in network security. By detecting the presence of network worms in the network, resources and services can be further protected by patching or installing security measures, such as firewalls, intrusion detection systems, or alternative computer systems.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Anbar, Mohammed F.R.
author_facet	Anbar, Mohammed F.R.
author_sort	Anbar, Mohammed F.R.
title	A Statistical Approach Towards Worm Detection Using Cross-Relation Technique
title_short	A Statistical Approach Towards Worm Detection Using Cross-Relation Technique
title_full	A Statistical Approach Towards Worm Detection Using Cross-Relation Technique
title_fullStr	A Statistical Approach Towards Worm Detection Using Cross-Relation Technique
title_full_unstemmed	A Statistical Approach Towards Worm Detection Using Cross-Relation Technique
title_sort	statistical approach towards worm detection using cross-relation technique
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2013
url	http://eprints.usm.my/43403/1/Mohammed%20F.R.%20Anbar24.pdf
_version_	1747821207090102272

A Statistical Approach Towards Worm Detection Using Cross-Relation Technique

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