Cross-Platform Malware Classification: Fusion of CNN and GRU Models
Bibliographic record
Abstract
Effective cross-platform malware categorization techniques are becoming more and more necessary as malware spreads across more systems.Conventional methods are primarily concerned with the static or dynamic aspects of malware, which often restricts their ability to identify and categorize malware on various operating systems.In this paper, we use both static and dynamic characteristics to present a unique deep learning-based method for cross-platform malware classification.Our work aims to identify the distinct features of malware on different operating systems, such as Windows, macOS, Android, and iOS.We provide a complete depiction of malware behavior by collecting both dynamic and static data, such as system calls and network traffic patterns, as well as file properties, API calls, and header information.Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) are two components of our deep learning architecture that we use to address the inherent issues of cross-platform malware categorization.This fusion of networks enables us to effectively capture both spatial and temporal patterns present in malware samples, enhancing the accuracy of classification across platforms.To evaluate the performance of our proposed model, we employ benchmark datasets encompassing diverse malware families across different operating systems.The results demonstrate superior classification accuracy, precision, recall, and F-score compared to traditional machine learning approaches and single-feature-based models.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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 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".