Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
cybersecurity
License:
File size: 2,980 Bytes
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---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- cybersecurity
pretty_name: DeepURLBench
size_categories:
- 10M<n<100M
---
# DeepURLBench
**DeepURLBench** is a large-scale benchmark dataset for real-world URL classification, developed by Deep Instinct's research team.
## ⚠️ Warning and Usage Disclaimer
This dataset contains real-world URLs labeled as **malware**, **phishing**, or **benign**, including domains that were associated with harmful or fraudulent activity at the time of collection. **Do not attempt to visit or interact with any of the URLs in this dataset.**
This dataset is intended **solely for research and educational purposes** in cybersecurity and machine learning. We **strongly recommend using it in a read-only context**, and not resolving or querying any of the included domains or IP addresses.
Deep Instinct assumes no responsibility for misuse of the dataset.
**DeepURLBench** is a large-scale benchmark dataset for real-world URL classification, developed by Deep Instinct's research team.
## Dataset Overview
The dataset includes two subsets in Parquet format:
### 🟢 `urls_with_dns`
Contains additional DNS resolution data:
- `url`: The URL being analyzed.
- `first_seen`: The timestamp when the URL was first observed.
- `TTL` (Time to Live): DNS TTL value.
- `label`: The classification label (`malware`, `phishing`, or `benign`).
- `ip_address`: List of resolved IP addresses.
### 🔵 `urls_without_dns`
Contains only the core metadata:
- `url`: The URL being analyzed.
- `first_seen`: The timestamp when the URL was first observed.
- `label`: The classification label (`malware`, `phishing`, or `benign`).
## Important Notes on Splitting
Although Hugging Face shows each loaded file under the `"train"` split by default, this dataset **does not include predefined train/validation/test splits**.
Instead, the intended splitting strategy is described in detail in our [paper](#citation). In brief, we recommend splitting the data **chronologically by the `first_seen` field**, so that evaluation is performed on newer, unseen URLs — simulating real-world deployment.
Each subset (`urls_with_dns` and `urls_without_dns`) is designed to be loaded independently, as shown below.
## How to Load
You can load the dataset using the Hugging Face `datasets` library:
```python
from datasets import load_dataset
ds_with_dns = load_dataset(
"DeepInstinct/DeepURLBench",
data_files="urls_with_dns.parquet"
)
ds_without_dns = load_dataset(
"DeepInstinct/DeepURLBench",
data_files="urls_without_dns.parquet"
)
```
## License
This dataset is available under the CC BY-NC 4.0 License.
## Citation
@misc{deepurlbench2025,
author = {Deep Instinct Research Team},
title = {DeepURLBench: A large-scale benchmark for URL classification},
year = {2025},
howpublished = {Available at: https://huggingface.co/datasets/DeepInstinct/DeepURLBench}
} |