--- license: cc-by-4.0 configs: - config_name: default data_files: - split: train path: "data/train.parquet" - split: test path: "data/train.parquet" task_categories: - text-classification - tabular-classification size_categories: - n<1K annotations_creators: - found tags: - phishing - url - security --- # Dataset Description The provided dataset includes **11430** URLs with **87** extracted features. The dataset are designed to be used as a benchmark for machine learning based **phishing detection** systems. The datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs. Features are from three different classes: - **56** extracted from the structure and syntax of URLs - **24** extracted from the content of their correspondent pages - **7** are extracetd by querying external services. ## Details - **Funded by:** Abdelhakim Hannousse, Salima Yahiouche - **Shared by:** [pirocheto](https://github.com/pirocheto) - **License:** [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) - **Paper:** [https://arxiv.org/abs/2010.12847](https://arxiv.org/abs/2010.12847) ## Source Data The diagram below illustrates the procedure for creating the corpus. For details, please refer to the paper.
Diagram source data

Source: Extract form the paper

## Load Dataset - With **datasets**: ```python from datasets import load_dataset dataset = load_dataset("pirocheto/phishing-url") ``` - With **pandas** and **huggingface_hub**: ```python import pandas as pd from huggingface_hub import hf_hub_download REPO_ID = "pirocheto/phishing-url" FILENAME = "data.csv" df = pd.read_csv( hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset") ) ``` - With **pandas** only: ```python import pandas as pd url = "https://huggingface.co/datasets/pirocheto/phishing-url/raw/main/data.csv" df = pd.read_csv(url) ``` ## Citation To give credit to the creators of this dataset, please use the following citation in your work: - BibTeX format ``` @article{Hannousse_2021, title={Towards benchmark datasets for machine learning based website phishing detection: An experimental study}, volume={104}, ISSN={0952-1976}, url={http://dx.doi.org/10.1016/j.engappai.2021.104347}, DOI={10.1016/j.engappai.2021.104347}, journal={Engineering Applications of Artificial Intelligence}, publisher={Elsevier BV}, author={Hannousse, Abdelhakim and Yahiouche, Salima}, year={2021}, month=sep, pages={104347} } ``` - APA format ``` Hannousse, A., & Yahiouche, S. (2021). Towards benchmark datasets for machine learning based website phishing detection: An experimental study. Engineering Applications of Artificial Intelligence, 104, 104347. ```