| --- |
| 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}, |
| } |
| ``` |
