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CONEXT
2007
ACM

Detecting worm variants using machine learning

9 years 26 days ago
Detecting worm variants using machine learning
Network intrusion detection systems typically detect worms by examining packet or flow logs for known signatures. Not only does this approach mean worms cannot be detected until the signatures are created, but that variants of known worms will remain undetected since they will have different signatures. The intuitive solution is to write more generic signatures. This solution, however, would increase the false alarm rate and is therefore practically not feasible. This paper reports on the feasibility of using a machine learning technique to detect variants of known worms in real-time. Support vector machines (SVMs) are a machine learning technique known to perform well at various pattern recognition tasks, such as text categorization and handwritten digit recognition. Given the efficacy of SVMs in standard pattern recognition problems this work applies SVMs to the worm detection problem. Specifically, we investigate the optimal configuration of SVMs and associated kernel functions...
Oliver Sharma, Mark Girolami, Joseph S. Sventek
Added 18 Oct 2010
Updated 18 Oct 2010
Type Conference
Year 2007
Where CONEXT
Authors Oliver Sharma, Mark Girolami, Joseph S. Sventek
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