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2007
Springer

Improving the Detection of Unknown Computer Worms Activity Using Active Learning

9 years 10 months ago
Improving the Detection of Unknown Computer Worms Activity Using Active Learning
Detecting unknown worms is a challenging task. Extant solutions, such as anti-virus tools, rely mainly on prior explicit knowledge of specific worm signatures. As a result, after the appearance of a new worm on the Web there is a significant delay until an update carrying the worm’s signature is distributed to anti-virus tools. We propose an innovative technique for detecting the presence of an unknown worm, based on the computer operating system measurements. We monitored 323 computer features and reduced them to 20 features through feature selection. Support vector machines were applied using 3 kernel functions. In addition we used active learning as a selective sampling method to increase the performance of the classifier, exceeding above 90% mean accuracy, and for specific unknown worms 94% accuracy.
Robert Moskovitch, Nir Nissim, Dima Stopel, Clint
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2007
Where KI
Authors Robert Moskovitch, Nir Nissim, Dima Stopel, Clint Feher, Roman Englert, Yuval Elovici
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