File size: 4,364 Bytes
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license: mit
task_categories:
- text-classification
- image-classification
language:
- en
size_categories:
- 1M<n<10M
configs:
- config_name: images
data_files:
- split: train
path: images/train-*
- split: validation
path: images/validation-*
- split: test
path: images/test-*
- config_name: pairs
data_files:
- split: train
path: pairs/train-*
- split: validation
path: pairs/validation-*
- split: test
path: pairs/test-*
dataset_info:
- config_name: images
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': real
'1': phishx
- name: text
dtype: string
- name: pair_id
dtype: int32
splits:
- name: train
num_bytes: 2660477680
num_examples: 1799810
- name: validation
num_bytes: 760218759
num_examples: 514232
- name: test
num_bytes: 379976401
num_examples: 257116
download_size: 3776742555
dataset_size: 3800672840
- config_name: pairs
features:
- name: domain
dtype: large_string
- name: homoglyphs
dtype: large_string
- name: pair_id
dtype: int32
splits:
- name: train
num_bytes: 49094585
num_examples: 899905
- name: validation
num_bytes: 14028004
num_examples: 257116
- name: test
num_bytes: 7010618
num_examples: 128558
download_size: 40293542
dataset_size: 70133207
tags:
- cybersecurity
- domain-spoofing
- homoglyph
- idn-homograph
- phishing
- security
pretty_name: GlyphNet Homoglyph Domains
---
# GlyphNet: Homoglyph Domains Dataset
Data for detecting [homoglyph](https://en.wikipedia.org/wiki/IDN_homograph_attack)
phishing domains (e.g. `facebook.com` spoofed with visually-similar Unicode
characters). Every genuine domain is paired with a synthetically generated
homoglyph variant, and each domain is also rendered to a 256x256 grayscale image
so the task can be tackled as text **or** image classification.
Paper: [arXiv:2306.10392](https://arxiv.org/abs/2306.10392) ·
Code: [github.com/Akshat4112/Glyphnet](https://github.com/Akshat4112/Glyphnet)
## Configs & splits
Both configs share the same `train` / `validation` / `test` split (70/20/10).
Splits are assigned **per pair** (seeded), so a genuine domain and its homoglyph
always fall in the same split - preventing leakage between near-identical pairs.
| Config | Rows | Fields |
|--------|------|--------|
| `pairs` | 1,285,579 pairs | `domain` (genuine), `homoglyphs` (spoofed), `pair_id` |
| `images` | 2,571,158 images | `image` (256x256 grayscale PNG), `label` (`real`/`phish`), `text`, `pair_id` |
`pair_id` links the two `images` rows (one `real`, one `phish`) that came from
the same source pair, and matches the `pair_id` in the `pairs` config.
```python
from datasets import load_dataset
pairs = load_dataset("Akshat4112/Glyphnet", "pairs")
images = load_dataset("Akshat4112/Glyphnet", "images")
train_img = images["train"] # or "validation" / "test"
```
## How it was generated
Homoglyphs are produced by substituting one or two characters with Unicode
confusables (`code/dataGeneration.py`). Images are rendered with the **DejaVu
Sans** font - Arial (used in the paper) is proprietary and not redistributed
here, so glyph shapes may differ slightly from the original figures.
## Intended uses & limitations
- Intended for research on homoglyph / IDN-homograph phishing detection.
- Homoglyphs are **synthetic**, not harvested from real attacks, so the
distribution may not match live phishing in the wild.
- The genuine and spoofed strings within a pair are near-identical; always
respect the provided splits (or group by `pair_id`) to avoid leakage.
- Rendering font differs from the paper (DejaVu Sans vs Arial).
## License
MIT (same as the source repository).
## Citation
If you use this dataset in your research, please cite the GlyphNet paper:
```bibtex
@article{gupta2023glyphnet,
title = {GlyphNet: Homoglyph domains dataset and detection using attention-based Convolutional Neural Networks},
author = {Gupta, Akshat and Tomar, Laxman Singh and Garg, Ridhima},
journal = {arXiv preprint arXiv:2306.10392},
year = {2023}
}
```
> Gupta, A., Tomar, L. S., & Garg, R. (2023). *GlyphNet: Homoglyph domains
> dataset and detection using attention-based Convolutional Neural Networks.*
> arXiv:2306.10392.
|