Graph-Oriented Modelling of Process Event Activity for the Detection of Malware
Bibliographic record
Abstract
This paper presents an approach to malware detection using Graph Neural Networks (GNN) to capture the complex relationships and dependencies between different components of an operating system (OS). Traditional methods for malware detection rely on known signatures of malware and may fail to detect new or modified malware variants. GNNs offer a promising solution by analyzing graph-structured data and identifying malicious behavior patterns. Specifically, this paper investigates the use of GNNs for malware detection based on the API call sequences of different event types, including File System, Registry, and File and Thread activity. The paper presents a representative dataset of host process activity of malware collected in a custom sandbox environment, comprising over 239 malware executions with randomly executed benignware samples. The paper then describes the GNN model trained on the dynamic process behavior generated from process execution graphs, with independent models developed based on each category of API events. Finally, the paper presents a trained model that maximizes the generalization performance of the model, demonstrating the applicability of GNNs for malware detection. This paper presents one of the first applications of GNN classification based on process hierarchy during malware execution that includes interaction with benignware as well.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".