N-Gram-Based Detection of New Malicious Code

Authors:
Tony Abou-Assaleh;Nick Cercone;Vlado Keselj;Ray Sweidan
Affiliations:
Dalhousie University;Dalhousie University;Dalhousie University;Dalhousie University
Venue:
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
Year:
2004

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Abstract

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.