Rupkumar2906/phreshphish

--- license: cc-by-4.0 size_categories: - 100K<n<1M task_categories: - text-classification pretty_name: PhreshPhish configs: - config_name: default data_files: - split: train path: "data/train-*.parquet" - split: test path: "data/test-*.parquet" --- # PhreshPhish PhreshPhish is a **large-scale**, **real-world** dataset and benchmark for phishing webpage detection containing phishing and benign HTML-URL pairs. - **Train** 498,255 samples: 276,729 benign and 221,526 phish - **Test** 168,060 samples: 91,260 benign and 76,876 phish - **Benchmarks** 975 benchmarks with base rates ranging from `[5e-4, 1e-3, 5e-3, 1e-2, 5e-2]` ## Changelog - **v1.0.1 (2026-02-07)**: Added ~200k new samples collected between March and December 2025, improved temporal consistency by downsampling some earlier samples - **v1.0.0 (2025-05-14)**: Initial release ## Getting Started ```python from datasets import load_dataset train = load_dataset('phreshphish/phreshphish', split='train') test = load_dataset('phreshphish/phreshphish', split='test') ``` ## License & Terms of Use The dataset is released under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) license and should only be used for anti-phishing research. ## Citing If you find our work useful, please consider citing. Paper: [PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark](https://huggingface.co/papers/2507.10854) ```bibtex @article{dalton2025phreshphish, title = {PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark}, author = {Thomas Dalton and Hemanth Gowda and Girish Rao and Sachin Pargi and Alireza Hadj Khodabakhshi and Joseph Rombs and Stephan Jou and Manish Marwah}, year = 2025, journal = {arXiv preprint}, url = {https://arxiv.org/abs/2507.10854}, eprint = {2507.10854} } ```