| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| tags: |
| - malware-detection |
| - 3D-CNN |
| - Morton-curve |
| - cybersecurity |
| - PE-executables |
| pretty_name: MorVis - 3D Volumetric Malware Tensors |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # MorVis: 3D Volumetric Malware Detection Tensors |
|
|
| ## Dataset Description |
|
|
| This dataset contains 6-channel 3D volumetric tensors (64×64×64) generated from Windows PE executables using Morton (Z-order) curve mapping. It accompanies the paper *"3D Volumetric Malware Detection Using Morton Curves and Multi-Channel Semantic Features"* (Harish et al., 2026). |
|
|
| ## Dataset Summary |
|
|
| - **Malware tensors**: Generated from the VirusShare_00499 dump (~28,000 samples) |
| - **Benign tensors**: Not included to save storage and download time (~150GB). Users can generate benign tensors using the provided script on their own installed applications. |
| - **Tensor shape**: `(6, 64, 64, 64)` per sample, saved as `.npy` files |
| - **Curve order**: 6 (64³ = 262,144 voxels) |
| |
| ## Channels |
| |
| Each tensor has 6 semantic channels: |
| |
| | Channel | Name | Description | |
| |---------|------|-------------| |
| | 0 | Raw bytes | Normalized byte values (0–1) | |
| | 1 | Entropy | Local Shannon entropy over a sliding window | |
| | 2 | Code mask | Binary mask for executable sections (.text, .code) | |
| | 3 | Import density | Proximity to import/IAT tables (behavioral signal) | |
| | 4 | String density | Fraction of printable ASCII in a local window | |
| | 5 | Data mask | Binary mask: 1 = real file bytes, 0 = padding | |
| |
| ## Generation Script |
| |
| `malware_3d_multichannel.py` is provided in this repository. Usage: |
| ```bash |
| python malware_3d_multichannel.py -i ./samples -o ./tensors --order 6 |
| ``` |
| |
| **Arguments:** |
| - `--input_dir / -i`: Directory containing PE files |
| - `--output_dir / -o`: Output directory for `.npy` tensors |
| - `--order`: Curve order (default: 6, giving 64³ grid) |
| - `--min_size`: Minimum file size in KB (default: 10) |
| - `--max_size`: Maximum file size in MB (default: 50) |
|
|
| The script parses PE headers, extracts relevant sections (skipping resources, relocations, debug), computes all 6 channels, maps bytes into 3D via Morton curve, and saves each tensor as a NumPy `.npy` file along with a `metadata.json`. |
|
|
| ## Generating Benign Tensors |
|
|
| Benign tensors are not hosted due to the prohibitive size (~150GB). To generate your own, run the script on locally installed applications: |
| ```bash |
| python malware_3d_multichannel.py -i "C:\Windows\System32" -o ./tensors_benign |
| python malware_3d_multichannel.py -i "C:\Program Files" -o ./tensors_benign |
| ``` |
|
|
| Any directory containing legitimate PE executables will work. |
|
|
| ## Source Data |
|
|
| - **Malware**: VirusShare_00499 dump (Windows PE executables) |
| - **Benign**: User-installed Windows applications and system files |
| |
| ## Citation |
| |
| If you use this dataset, please cite (paper currently under review): |
| ```bibtex |
| @article{harish2026morvis, |
| title={3D Volumetric Malware Detection Using Morton Curves and Multi-Channel Semantic Features}, |
| author={Harish, Parikshieth and P.S., Ramesh and C, Suganthan}, |
| year={2026} |
| } |
| ``` |
| |
| ## Authors |
| |
| - Parikshieth Harish (parikshieth.harish2023@vitstudent.ac.in) |
| - Ramesh P.S. — Corresponding Author (ramesh.ps@vit.ac.in) |
| - Suganthan C (suganthan.c@vit.ac.in) |
| |
| School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India |
