A Scalable Malware Classification based on Integrated Static and Dynamic Features

This paper presents a malware classification approach which aims to improve precision and support scalability. To this end, a hybrid approach combining both static and dynamic features is adopted. The hybrid approach has the advantage of being a complete and robust solution to evasion techniques used by malware writers. The proposed methodology allowed achieving a very promising accuracy of 99.41% in classifying malware into families while considerably reducing the feature space compared to competing approaches in the literature