A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation

TL;DR

This paper presents a comprehensive research portfolio on GNN-based malware detection, focusing on scalability, interpretability, dataset curation, and introducing new methods and explanations to advance the field.

Contribution

It combines surveys, novel graph reduction techniques, explanation methods, ensemble models, and curated datasets to improve GNN malware detection and interpretability.

Findings

Enhanced explainability with dual subgraph matching techniques.

Improved model interpretability using attention-guided GNN ensembles.

Curated datasets facilitate reproducibility and future research.

Abstract

Graph Neural Networks (GNNs) have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings together six related studies that collectively address these issues. The portfolio begins with a survey of graph-based malware detection and explainability, then advances to new graph reduction methods, integrated reduction-learning approaches, and investigations into the consistency of explanations. It also introduces dual explanation techniques based on subgraph matching and develops ensemble-based models with attention-guided stacked GNNs to improve interpretability. In parallel, curated datasets of control flow graphs are released to support reproducibility and enable future research. Together, these contributions form a coherent line of research that strengthens GNN-based malware detection by enhancing efficiency, increasing transparency, and providing solid experimental foundations.