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---
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},
}
``` |