Discriminant features for metamorphic malware detection

To unfold a solution for the detection of metamorphic viruses (obfuscated malware), we propose a non signature based approach using feature selection techniques such as Categorical Proportional Difference (CPD), Weight of Evidence of Text (WET), Term Frequency-Inverse Document Frequency (TF-IDF) and Term Frequency-Inverse Document Frequency-Class Frequency (TF-IDF-CF). Feature selection methods are employed to rank and prune bi-gram features obtained from malware and benign files. Synthesized features are further evaluated for their prominence in either of the classes. Using our proposed methodology 100% accuracy is obtained with test samples. Hence, we argue that the statistical scanner proposed by us can identify future metamorphic variants and can assist antiviruses with high accuracy.