A Survey of Malware Classification Methods Based on Data Flow Graph

Abstract

Malware is emerging day by day. To evade detection, many malware obfuscation techniques have emerged. Dynamic malware detection methods based on data flow graphs have attracted much attention since they can deal with the obfuscation problem to a certain extent. Many malware classification methods based on data flow graphs have been proposed. Some of them are based on user-defined features or graph similarity of data flow graphs. Graph neural networks have also recently been used to implement malware classification recently. This paper provides an overview of current data flow graph-based malware classification methods. Their respective advantages and disadvantages are summarized as well. In addition, the future trend of the data flow graph-based malware classification method is analyzed, which is of great significance for promoting the development of malware detection technology.