Create README.md

README.md

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+---
+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 due to licensing — generate them yourself using the provided script on Windows system files
+- **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
+
+To reproduce the benign class from the paper, run the script on Windows system 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
+```
+
+## 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:
+```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