README.md

---
license: cc-by-nc-4.0
task_categories:
- text-classification
pretty_name: DeepURLBench
configs:
- config_name: urls_with_dns
  data_files:
  - split: train
    path: "data/urls_with_dns/*.parquet"
- config_name: urls_without_dns
  data_files:
  - split: train
    path: "data/urls_without_dns/*.parquet"
---

# DeepURLBench Dataset

**note** README copied from source repo: https://github.com/deepinstinct-algo/DeepURLBench

This repository contains the dataset **DeepURLBench**, introduced in the paper **"A New Dataset and Methodology for Malicious URL Classification"** by Deep Instinct's research team.

## Dataset Overview

The repository includes two parquet directories:

1. **`urls_with_dns`**:
   - Contains the following fields:
     - `url`: The URL being analyzed.
     - `first_seen`: The timestamp when the URL was first observed.
     - `TTL` (Time to Live): The time-to-live value of the DNS record.
     - `label`: Indicates whether the URL is malware, phishing or benign.
     - `IP addresses`: The associated IP addresses.

2. **`urls_without_dns`**:
   - Contains the following fields:
     - `url`: The URL being analyzed.
     - `first_seen`: The timestamp when the URL was first observed.
     - `label`: Indicates whether the URL is malware, phishing or benign.

## Usage Instructions

To load the dataset using Python and Pandas, follow these steps:

```python
import pandas as pd

# Replace 'directory' with the path to the parquet file or directory
df = pd.DataFrame.from_parquet("directory")
```
## License

This dataset is licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/). You are free to use, share, and adapt the dataset for non-commercial purposes, with proper attribution.

## Citation

```bibtex
@misc{schvartzman2024newdatasetmethodologymalicious,
      title={A New Dataset and Methodology for Malicious URL Classification}, 
      author={Ilan Schvartzman and Roei Sarussi and Maor Ashkenazi and Ido kringel and Yaniv Tocker and Tal Furman Shohet},
      year={2024},
      eprint={2501.00356},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2501.00356}, 
}
```