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A Data Mining Approach for Detection of Self-Propagating Worms

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4 Author(s)
Marhusin, M.F. ; Univ. of New South Wales at ADFA, Canberra, ACT, Australia ; Lokan, C. ; Larkin, H. ; Cornforth, D.

In this paper we demonstrate our signature based detector for self-propagating worms. We use a set of worm and benign traffic traces of several endpoints to build benign and worm profiles. These profiles were arranged into separate n-ary trees. We also demonstrate our anomaly detector that was used to deal with tied matches between worm and benign trees. We analyzed the performance of each detector and also with their integration. Results show that our signature based detector can detect very high true positive. Meanwhile, the anomaly detector did not achieve high true positive. Both detectors, when used independently, suffer high false positive. However, when both detectors were integrated they maintained a high detection rate of true positive and minimized the false positive.

Published in:

Network and System Security, 2009. NSS '09. Third International Conference on

Date of Conference:

19-21 Oct. 2009