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Detection of New Malicious Code Using N-grams Signatures

Tony Abou-Assaleh, Nick Cercone, Vlado Keselj, Ray Sweidan
Proceedings of Second Annual Conference on Privacy, Security and Trust, October 13-15, 2004
October 2004

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Abstract

Signature-based malicious code detection is the standard technique in all commercial anti-virus software. This method can detect a virus only after the virus has appeared and caused damage. Signature-based detection performs poorly when attempting to identify new viruses. Motivated by the standard signature-based technique for detecting viruses, and a recent successful text classification method, n-grams analysis, we explore the idea of automatically detecting new malicious code. We employ n-grams analysis to automatically generate signatures from malicious and benign software collections. The n-grams-based signatures are capable of classifying unseen benign and malicious code. The datasets used are large compared to earlier applications of n-grams analysis.

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