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N-gram-based detection of new malicious code

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4 Author(s)
Abou-Assaleh, T. ; Privacy & Security Lab., Dalhousie Univ., Halifax, NS, Canada ; Cercone, N. ; Keselj, V. ; Sweidan, R.

The current commercial anti-virus software detects a virus only after the virus has appeared and caused damage. Motivated by the standard signature-based technique for detecting viruses, and a recent successful text classification method, we explore the idea of automatically detecting new malicious code using the collected dataset of the benign and malicious code. We obtained accuracy of 100% in the training data, and 98% in 3-fold cross-validation.

Published in:

Computer Software and Applications Conference, 2004. COMPSAC 2004. Proceedings of the 28th Annual International  (Volume:2 )

Date of Conference:

28-30 Sept. 2004