FCGAT: Interpretable Malware Classification Method using Function Call Graph and Attention Mechanism

Abstract

—Malware classification facilitates static analysis, which is manually intensive but necessary work to understand the inner workings of unknown malware. Machine learning based approaches have been actively studied and have great potential. However, their drawback is that their models are considered black boxes and are challenging to explain their classification results and thus cannot provide patterns specific to malware. To address this problem, we propose FCGAT, the first malware classification method that provides interpretable classification reasons based on program functions. FCGAT applies natural language processing techniques to create function features and updates them to reflect the calling relationships between functions. Then, it applies attention mechanism to create malware feature by emphasizing the functions that are important for classification with attention weights. FCGAT provides an importance ranking of functions based on attention weights as an explanation. We evaluate the performance of FCGAT on two datasets. The results show that the F1-Scores are 98.15% and 98.18%, which are competitive with the cutting-edge methods. Furthermore, we examine how much the functions emphasized by FCGAT contribute to the classification. Surprisingly, our result show that only top 6 (average per sample) highly-weighted functions yield as much as 70% accuracy. We also show that these functions reflect the characteristics of malware by analyzing them. FCGAT can provide analysts with reliable explanations using a small number of functions. These explanations could bring various benefits, such as improved efficiency in malware analysis and comprehensive malware trend analysis.