Explainable malware detection through integrated graph reduction and learning techniques
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
Recently, Control Flow Graphs and Function Call Graphs have gain attention in malware detection task due to their ability in representation the complex structural and functional behavior of programs. To better utilize these representations in malware detection and improve the detection performance, they have been paired with Graph Neural Networks (GNNs). However, the sheer size and complexity of these graph representation poses a significant challenge for researchers. At the same time, a simple binary classification provided by the GNN models is insufficient for malware analysts. To address these challenges, this paper integrates novel graph reduction techniques and GNN explainability in to a malware detection framework to enhance both efficiency and interpretability. Through our extensive evolution, we demonstrate that the proposed graph reduction technique significantly reduces the size and complexity of the input graphs, while maintaining the detection performance. Furthermore, the extracted important subgraphs using the GNNExplainer, provide better insights about the model's decision and help security experts with their further analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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