A Statistical Approach Towards Worm Detection Using Cross-Relation Technique

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Date
2013-03
Authors
Anbar, Mohammed F.R.
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Publisher
Universiti Sains Malaysia
Abstract
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. Existing approaches in network worm detection and principally behavior based approaches are not sufficiently accurate to perform network worm detection due to simple heuristics and inefficient approaches used to detect network symptoms. The intention of this thesis is to propose and implement an approach that detects the existence of network worms in the network.
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Keywords
Worm Detection , Cross-Relation Technique
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