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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| Format: | Thesis |
| Language: | English |
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2013
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| Online Access: | http://eprints.usm.my/43403/ http://eprints.usm.my/43403/1/Mohammed%20F.R.%20Anbar24.pdf |
| _version_ | 1848879804420456448 |
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| author | Anbar, Mohammed F.R. |
| author_facet | Anbar, Mohammed F.R. |
| author_sort | Anbar, Mohammed F.R. |
| building | USM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T17:53:03Z |
| format | Thesis |
| id | usm-43403 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T17:53:03Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-434032019-04-12T05:26:17Z http://eprints.usm.my/43403/ A Statistical Approach Towards Worm Detection Using Cross-Relation Technique 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 NonPeerReviewed application/pdf en http://eprints.usm.my/43403/1/Mohammed%20F.R.%20Anbar24.pdf Anbar, Mohammed F.R. (2013) A Statistical Approach Towards Worm Detection Using Cross-Relation Technique. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Anbar, Mohammed F.R. A Statistical Approach Towards Worm Detection Using Cross-Relation Technique |
| title | 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_short | A Statistical Approach Towards Worm Detection Using Cross-Relation Technique |
| title_sort | statistical approach towards worm detection using cross-relation technique |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/43403/ http://eprints.usm.my/43403/1/Mohammed%20F.R.%20Anbar24.pdf |