Malware Function Classification Using APIs in Initial Behavior
Naoto Kawaguchi, Kazumasa Omote · 2015
Malware proliferation has become a serious threat to the Internet in recent years. Most of the current malware are subspecies of existing malware that have been automatically generated by illegal tools. To conduct an efficient analysis of malware, estimating their functions in advance is effective when we give priority to analyze. However, estimating malware functions has been difficult due to the increasing sophistication of malware. Although various approaches for malware detection and classification have been considered, the classification accuracy is still low. In this paper, we propose a new classification method which estimates malware's functions from APIs observed by dynamic analysis on a host. We examining whether the proposed method can correctly classify unknown malware based on function by machine learning. The results show that the our new method can classify each malware's function with an average accuracy of 83.4%.