Towards the Detection of Undetectable Metamorphic Malware
Jikku Kuriakose, P. Vinod · 2014
Our research developed a non signature based approach, employing feature selection methods such as Categorical Proportional Distance (CPD), Weight of Evidence of Text (WET), Term Frequency - Inverse Document Frequency (TF-IDF), Term Frequency - Inverse Document Frequency - Class Frequency (TF-IDF-CF), Galavotti-Sebastiani-Simi Coefficient (GSS) and Term Significance (TS). Classification model is developed by considering bi--gram features ranked with these feature selection techniques. The proposed feature selection approaches detect unseen malware samples with accuracy in the range of 99% to 100%. Relevance of a feature ranking methods on variable feature length is ascertained using McNemar test.