N-gram-based detection of new malicious code

  title={N-gram-based detection of new malicious code},
  author={Tony Abou-Assaleh and Nick Cercone and Vlado Keselj and Ray Sweidan},
  journal={Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004.},
  pages={41-42 vol.2}
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. 
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