Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection
Malware detection is a critical task in cybersecurity, and graph neural networks over control flow graphs have shown promising results for it. However, detectors are usually evaluated on a random split of a corpus collected over a single period, which cannot show how well a model generalizes to later samples. This study addresses that limitation with a strict temporal split: every model is trained on one period and scored once on a later one. Two corpora of control flow graphs, each node carrying 37 features, were extracted statically from 1,989 Windows portable executables: 459 graphs from 2024-2025 for training and 223 from 2026 for evaluation. Twelve variants and a flat-feature control...
| Authors | Md. Asif Sajeed, Md. Nazrul Islam Mondal, Md Ashraful Hossen Akash |
|---|---|
| Primary category | cs.CR |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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