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--- |
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license: cc-by-nc-4.0 |
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task_categories: |
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- text-classification |
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pretty_name: DeepURLBench |
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configs: |
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- config_name: urls_with_dns |
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data_files: |
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- split: train |
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path: "data/urls_with_dns/*.parquet" |
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- config_name: urls_without_dns |
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data_files: |
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- split: train |
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path: "data/urls_without_dns/*.parquet" |
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--- |
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# DeepURLBench Dataset |
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**note** README copied from source repo: https://github.com/deepinstinct-algo/DeepURLBench |
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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. |
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## Dataset Overview |
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The repository includes two parquet directories: |
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1. **`urls_with_dns`**: |
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- Contains the following fields: |
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- `url`: The URL being analyzed. |
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- `first_seen`: The timestamp when the URL was first observed. |
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- `TTL` (Time to Live): The time-to-live value of the DNS record. |
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- `label`: Indicates whether the URL is malware, phishing or benign. |
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- `IP addresses`: The associated IP addresses. |
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2. **`urls_without_dns`**: |
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- Contains the following fields: |
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- `url`: The URL being analyzed. |
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- `first_seen`: The timestamp when the URL was first observed. |
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- `label`: Indicates whether the URL is malware, phishing or benign. |
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## Usage Instructions |
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To load the dataset using Python and Pandas, follow these steps: |
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```python |
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import pandas as pd |
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# Replace 'directory' with the path to the parquet file or directory |
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df = pd.DataFrame.from_parquet("directory") |
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``` |
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## License |
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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. |
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## Citation |
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```bibtex |
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@misc{schvartzman2024newdatasetmethodologymalicious, |
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title={A New Dataset and Methodology for Malicious URL Classification}, |
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author={Ilan Schvartzman and Roei Sarussi and Maor Ashkenazi and Ido kringel and Yaniv Tocker and Tal Furman Shohet}, |
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year={2024}, |
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eprint={2501.00356}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG}, |
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url={https://arxiv.org/abs/2501.00356}, |
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} |
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``` |
