Feature selection for malware classification

Abstract

In this paper, we perform an analysis to select significant features which are useful for classifying malware. Our approach is to reduce significantly the number of features from 390 to 4 based on MANOVA, which explains 99.4% variation of the data. With the selected features, we classify malware samples and have achieved 96.2% of accuracy and 0.6% of false positive.