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Dual explanations via subgraph matching for malware detection
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DOI：10.1016/j.engappai.2026.115049.png)
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Abstract
En 中文
• Dual prototype-driven framework for explainable GNN-based malware detection. • SubMatch explainer uses subgraph matching for structure-aware node interpretation. • Behavior-aligned explanations via verified malicious and benign subgraph prototypes. • Fine-grained localization of malicious and benign regions within a CFG.
Keywords:
Interpretable malware detection
Graph Neural Networks
Subgraph matching
Explainable artificial intelligence
Machine learning
SubMatch explainer
Dual explainability
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