README.md

metadata

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 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 · Code: 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.

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:

@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.