| --- |
| license: cc-by-4.0 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.parquet |
| - split: test |
| path: data/test.parquet |
| task_categories: |
| - text-classification |
| - tabular-classification |
| - token-classification |
| - text2text-generation |
| size_categories: |
| - n<1K |
| annotations_creators: |
| - found |
| tags: |
| - phishing |
| - url |
| - security |
| language: |
| - en |
| pretty_name: TabNetone |
| --- |
| |
|
|
|
|
| # 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. |
|
|
|
|
| The dataset was partitioned randomly into training and testing sets, with a ratio of **two-thirds for training** and **one-third for testing**. |
|
|
| ## 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. |
|
|
| <div align="center"> |
| <img src="images/source_data.png" alt="Diagram source data"> |
| </div> |
|
|
| <p align="center"> |
| <em>Source: Extract form the <a href="https://arxiv.org/abs/2010.12847">paper</a></em> |
| </p> |
|
|
| ## 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/train.parquet" |
| |
| df = pd.read_parquet( |
| 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/resolve/main/data/train.parquet" |
| df = pd.read_parquet(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. |
| ``` |