README.md

metadata

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:

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:

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):

@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