A Comprehensive Review of Graph Neural Networks for Malware Classification Pipelines: Architectures, Robustness, and Intelligent Security Applications
Bibliographic record
Abstract
The rapid evolution of cyber threats, particularly malware, has driven the need for advanced detection and classification techniques capable of handling complex and dynamic attack patterns. Traditional signature-based and heuristic approaches are increasingly ineffective against polymorphic and zero-day malware, creating a demand for more adaptive solutions. In this context, Graph Neural Networks (GNNs) have emerged as a powerful paradigm for modeling structured relationships in malware data, including function call graphs, network traffic flows, and system interactions. Unlike conventional machine learning models, GNNs capture non-Euclidean relationships, enabling superior representation learning and improved classification accuracy. This review analyzes GNN-based malware classification pipelines, focusing on architectural designs, robustness strategies, and security applications. It highlights the integration of graph construction methods, feature extraction, and classification models, while also examining issues such as adversarial robustness, scalability, and explainability. Findings suggest that models like Graph Convolutional Networks, Graph Attention Networks, and hybrid approaches outperform traditional deep learning techniques by leveraging relational dependencies. Despite their promise, challenges such as computational overhead, dataset limitations, and adversarial vulnerabilities remain, indicating the need for lightweight, interpretable, and privacy-preserving GNN frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".