Malware Authorship Attribution Model using Runtime Modules based on Automated Analysis

Abstract

Malware authorship attribution is a research field that identifies the author of malware by extracting and analyzing features that relate the authors from the source code or binary code of malware. Currently, it is being used as one of the detection techniques based on malware forensics or identifying patterns of continuous attacks such as APT attacks. The analysis methods to identify the author are as follows. One is a source code-based analysis method that extracts features from the source code, and the other is a binary-based analysis method that extracts features from the binary. However, to handle the modularization and the increasing amount of malicious code with these methods, both time and manpower are insufficient to figure out the characteristics of the malware. Therefore, we propose the model for malware authorship attribution by rapidly extracting and analyzing features using automated analysis. Automated analysis uses a tool and can be analyzed through a file of malware and the specific hash values without experts. Furthermore, it is the fastest to figure out among other malware analysis methods. We have experimented by applying various machine learning classification algorithms to six malware author groups, and Runtime Modules and Kernel32.dll API extracted from the automated analysis were selected as features for author identification. The result shows more high accuracy than the previous studies. By using the automated analysis, it extracts features of malware faster than source code and binary-based analysis methods.

Keywords

• malware authorship attribution
• automated analysis
• runtime modules
• machine learning classification.