In today’s digital age, malware constantly evolves and becomes more sophisticated. Traditional malware identification techniques are not designed to address the threats posed by evolving next-generation malware. The threats include, but are not limited to, system damage, data theft, privacy breach, financial loss or disruption of operations. Deep learning techniques can be used to detect and classify this new generation of malware. Geometric deep learning (GDL) methods leverage graph neural networks (GNNs) and are recognized for their enhanced representation learning and superior generalization capabilities compared to conventional Deep learning (DL) approaches. Experiments in this study assess the effectiveness of GDL algorithms for malware identification. Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) networks are contrasted with three GNN models: Graph Convolutional Network (GCN), Graph Attention Network (GAT), and GraphSAGE Network (GraphSAGE). The findings demonstrate that two out of three GDL models, GCN and GraphSAGE, except for GAT, outperform with a significant gain under various conditions that are proven in experiments. The research demonstrates the superior performance of GDL techniques over traditional DL for effective next-generation malware identification.
Channa et al. (Mon,) studied this question.