Using deep graph learning to improve dynamic analysis-based malware detection in PE files

Detecting zero-day malware in Windows PE files using dynamic analysis techniques has proven to be far more effective than traditional signature-based methods. One specific approach that has emerged in recent years is the use of graphs to represent executable behavior, which can be subsequently used to learn patterns. However, many current graph representations omit key parameter information, meaning that the behavioral impact of variable changes cannot be reliably understood. To combat these shortcomings, we present a new method for malware detection by applying a graph attention network on multi-edge directional heterogeneous graphs constructed from API calls. The experiments show the TPR and FPR scores demonstrated by our model, achieve better performance than those from other related works.