---
dataset_info:
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 39121933529
    num_examples: 375057861
  - name: validation
    num_bytes: 2059120752
    num_examples: 19739888
  download_size: 27303330107
  dataset_size: 41181054281
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
tags:
- URLs
- Domain Names
- Domain Generation Algorithms
- DGA
size_categories:
- 100M<n<1B
---

## Datasets Overview
The dataset URLs and Domain Names are collected from the following sources:
### mC4
- **Description**: The Multilingual Colossal Common Crawl Corpus (mC4) is a cleaned version of the Common Crawl's web corpus, curated by the Allen Institute for Artificial Intelligence. It contains approximately 170 million URLs.
- **Source**: [mC4 Dataset on Hugging Face](https://huggingface.co/datasets/legacy-datasets/mc4)

### falcon-refinedweb
- **Description**: An English large-scale dataset curated for large language model pre-training, compiled from CommonCrawl using strict filtering and extensive deduplication. It contains around 128 million URLs.
- **Source**: [Falcon-RefinedWeb](https://huggingface.co/datasets/legacy-datasets/falcon-refinedweb)

### CBA Web tracking datasets
- **Description**: Compiled by the Broadband Communications Systems and Architectures Research Group, this dataset contains 76 million URLs and 1.5 million domain names focused on web tracking.
- **Source**: [CBA Web Tracking Datasets](https://cba.upc.edu/downloads/category/29-web-tracking-datasets#)

### Tranco top 1M
- **Description**: A dataset of top 1M domain names, compiled and ranked by Tranco.
- **Source**: [Tranco List](https://tranco-list.eu/)

### UTL_DGA22
- **Description**: A Domain Generation Algorithm (DGA) botnet dataset containing 4.3 million entries from 76 DGA families.
- **Source**: [UTL_DGA22](https://github.com/tongtuan92/UTL_DGA22-Dataset)

### UMUDGA
- **Description**: A dataset for profiling DGA-based botnets, consisting of 30 million manually labeled DGA entries.
- **Source**: [UMUDGA](https://github.com/Cyberdefence-Lab-Murcia/UMUDGA)

## Abstract
Detecting and classifying suspicious or malicious domain names and URLs is fundamental task in cybersecurity. To leverage such indicators of compromise, cybersecurity vendors and practitioners often maintain and update blacklists of known malicious domains and URLs. However, blacklists frequently fail to identify emerging and obfuscated threats. Over the past few decades, there has been significant interest in developing machine learning models that automatically detect malicious domains and URLs, addressing the limitations of blacklists maintenance and updates. In this paper, we introduce DomURLs_BERT, a pre-trained BERT-based encoder adapted for detecting and classifying suspicious/malicious domains and URLs. DomURLs_BERT is pre-trained using the Masked Language Modeling (MLM) objective on a large multilingual corpus of URLs, domain names, and Domain Generation Algorithms (DGA) dataset. In order to assess the performance of DomURLs_BERT, we have conducted experiments on several binary and multi-class classification tasks involving domain names and URLs, covering phishing, malware, DGA, and DNS tunneling. The evaluations results show that the proposed encoder outperforms state-of-the-art character-based deep learning models and cybersecurity-focused BERT models across multiple tasks and datasets. The pre-training dataset, the pre-trained DomURLs_BERT encoder, and the experiments source code are publicly available.
## Citation

```bibtex
@article{domurlsbert2024,
  title={{DomURLs\_BERT}: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classification},
  author={Abdelkader {El Mahdaouy} and Salima Lamsiyah and Meryem {Janati Idrissi} and Hamza Alami and Zakaria Yartaoui and Ismail Berrada},
  journal={arXiv preprint arXiv:2409.09143},
      year={2024},
      eprint={2409.09143},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2409.09143}, 
}
```


Please cite the following papers also when using the dataset:

```bibtex
@article{2019t5,
  title={Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
  author={Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
  journal={Journal of Machine Learning Research},
  year={2020},
  url={https://jmlr.org/papers/v21/20-074.html}
}

@article{refinedweb,
  title={Refining Large-Scale Web Datasets Using Deep Learning and Human Review},
  author={Lastname, Firstname},
  journal={Journal Title},
  year={Year},
  url={https://someurl.com}
}

@inproceedings{pochat2018tranco,
  title={Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation},
  author={Victor Le Pochat and Tom Van Goethem and Samaneh Tajalizadehkhoob and Maciej Korczyński and Wouter Joosen},
  booktitle={Network and Distributed System Security Symposium},
  year={2019},
  url={https://www.ndss-symposium.org/ndss-paper/auto-draft-15/}
}

@article{tuan2023utldga22,
  title={UTL_DGA22: A Comprehensive Dataset for Domain Generation Algorithm Detection},
  author={Author Name},
  journal={Journal Name},
  year={2023},
  url={https://anotherurl.com}
}

@article{ZAGO2020105400,
  title={UMUDGA: A Dataset for Profiling DGA-Based Botnets},
  author={Zago, Matteo},
  journal={Computers & Security},
  year={2020},
  volume={92},
  pages={101540},
  publisher={Elsevier},
  doi={10.1016/j.cose.2020.101540}
}
```