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.
['Tony Abou-Assaleh', 'Nick Cercone', 'Vlado Keselj', 'Ray Sweidan']
N-gram-based detection of new malicious code