An Efficiend Method for Malware Classification using Feature Selection

Abstract

Malwares are growing exponentially in number, and authors of malwares are continuously releasing new ones. Malwares developed by the same author group might have similar signatures. For a number of applications including digital forensic and law enforcement, such characteristics can be used to determine which author group is likely to have released a given malware. So, the researchers developed a malware group classification study using data mining techniques. However, it has a drawback that it takes a lot of time by a number of features. In this paper, we propose a reduction in classification time using feature selection techniques. And using influential features associated with classification, we expect to give good insight into malware analysis. We evaluate our approach through extensive experiments with a real-world dataset labeled by a group of domain experts. The results show that our approach is efficient and provides good accuracy in malware classification.