9 papers
Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection
Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins +1
Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks (GNNs) h…
Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection
Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins +1
Mixture-of-Experts (MoE) offers flexible graph reasoning by combining multiple views of a graph through a learned router. We investigate routing-aware explanations for MoE graph mo…
Transparent Malware Detection With Granular Assembly Flow Explainability via Graph Neural Networks
Griffin Higgins, Roozbeh Razavi-Far, Hossein Shokouhinejad +1
As malware continues to become increasingly sophisticated, threatening, and evasive, malware detection systems must keep pace and become equally intelligent, powerful, and transpar…
A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation
Hossein Shokouhinejad, Griffin Higgins, Roozbeh Razavi-Far +1
Graph Neural Networks (GNNs) have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challen…
On the Consistency of GNN Explanations for Malware Detection
Hossein Shokouhinejad, Griffin Higgins, Roozbeh Razavi-Far +2
Control Flow Graphs (CFGs) are critical for analyzing program execution and characterizing malware behavior. With the growing adoption of Graph Neural Networks (GNNs), CFG-based re…
Recent Advances in Malware Detection: Graph Learning and Explainability
Hossein Shokouhinejad, Roozbeh Razavi-Far, Hesamodin Mohammadian +4
The rapid evolution of malware has necessitated the development of sophisticated detection methods that go beyond traditional signature-based approaches. Graph learning techniques…