Enhancing GNN Explanations for Malware Detection with Dual Subgraph Matching
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing sophistication of malware has challenged the effectiveness of conventional detection techniques, motivating the adoption of Graph Neural Networks (GNNs) for their ability to model the structural and semantic information embedded in control flow graphs. While GNNs offer high detection performance, their lack of transparency limits their applicability in security-critical domains. To address this, we present an explainable malware detection framework, which contains a dual explainer. This dual explainer integrates a GNN explainer with a neural subgraph matching approach and the VF2 algorithm. The proposed method identifies and verifies discriminative subgraphs during training, which are later used to explain new predictions through efficient matching. To enhance the generalization of the neural subgraph matcher, we train it using curriculum learning, gradually increasing subgraph complexity to improve matching quality. Experimental evaluations on benchmark datasets demonstrate that the proposed framework retains high classification accuracy while significantly improving interpretability. By unifying explainable graph learning techniques with subgraph matching, the proposed framework enables analysts to gain actionable insights, fostering greater trust in GNN-based malware detectors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it