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Managing the threat posed by malware requires accurate detection andclassification techniques. Traditional detection strategies, such as signaturescanning, rely on manual analysis of malware to extract relevant features,which is labor intensive and requires expert knowledge. Function call graphsconsist of a set of program functions and their inter-procedural calls,providing a rich source of information that can be leveraged to classifymalware without the labor intensive feature extraction step of traditionaltechniques. In this research, we treat malware classification as a graphclassification problem. Based on Local Degree Profile features, we train a widerange of Graph Neural Network (GNN) architectures to generate embeddings whichwe then classify. We find that our best GNN models outperform previouscomparable research involving the well-known MalNet-Tiny Android malwaredataset. In addition, our GNN models do not suffer from the overfitting issuesthat commonly afflict non-GNN techniques, although GNN models require longertraining times.

A Comparison of Graph Neural Networks for Malware Classification