What is it about?

The scope of this research is computer worm detection. Computer worm has been defined as a process that can cause a possibly evolved copy of it to execute on a remote computer. It does not require human intervention to propagate neither does it attach itself to an existing computer file. It spreads very rapidly. Modern computer worm authors obfuscate the code to make it difficult to detect the computer worm. This research proposes to use machine learning methodology for the detection of computer worms. More specifically, ensembles are used. The research deviates from existing detection approaches by using dark space network traffic attributed to an actual worm attack to train and validate the machine learning algorithms. It is also obtained that the various ensembles perform comparatively well. Each of them is therefore a candidate for the final model. The algorithms also perform just as well as similar studies reported in the literature.

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Why is it important?

Optimum feature set for worm detection that advances computer worm detection in networks


This work analyzes empirically performance of ensembles on selected features to detect computer worms. It is obtained that various ensembles perform equally well apart from the fact that tree-based ensembles overfit.

Nelson Ochieng Odunga
Strathmore University

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This page is a summary of: Optimizing Computer Worm Detection Using Ensembles, Security and Communication Networks, April 2019, Hindawi Publishing Corporation, DOI: 10.1155/2019/4656480.
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