Model Card for Model ID
DomURLs_BERT is a pre-trained BERT-based encoder adapted for detecting and classifying suspicious/malicious domains and URLs. DomURLs_BERT is pre-trained using the Masked Language Modeling (MLM) objective on a large multilingual corpus of URLs, domain names, and Domain Generation Algorithms (DGA) dataset.
Paper Abstract
Detecting and classifying suspicious or malicious domain names and URLs is fundamental task in cybersecurity. To leverage such indicators of compromise, cybersecurity vendors and practitioners often maintain and update blacklists of known malicious domains and URLs. However, blacklists frequently fail to identify emerging and obfuscated threats. Over the past few decades, there has been significant interest in developing machine learning models that automatically detect malicious domains and URLs, addressing the limitations of blacklists maintenance and updates. In this paper, we introduce DomURLs_BERT, a pre-trained BERT-based encoder adapted for detecting and classifying suspicious/malicious domains and URLs. DomURLs_BERT is pre-trained using the Masked Language Modeling (MLM) objective on a large multilingual corpus of URLs, domain names, and Domain Generation Algorithms (DGA) dataset. In order to assess the performance of DomURLs_BERT, we have conducted experiments on several binary and multi-class classification tasks involving domain names and URLs, covering phishing, malware, DGA, and DNS tunneling. The evaluations results show that the proposed encoder outperforms state-of-the-art character-based deep learning models and cybersecurity-focused BERT models across multiple tasks and datasets. The pre-training dataset, the pre-trained DomURLs_BERT encoder, and the experiments source code are publicly available.
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Model type: BERT-based Encoder.
- Language(s) (CyberSecurity): all.
Model Sources [optional]
- Repository: https://github.com/AbdelkaderMH/DomURLs_BERT
- Paper [optional]: [https://arxiv.org/pdf/2409.09143]
Citation
@article{ElMahdaouy2026DomURLsBERT,
author = {El Mahdaouy, Abdelkader and
Lamsiyah, Salima and
Janati Idrissi, Meryem and
Alami, Hamza and
Yartaoui, Zakaria and
Berrada, Ismail},
title = {{DomURLs\_BERT}: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classification},
journal = {Journal of Network and Systems Management},
year = {2026},
volume = {34},
number = {2},
pages = {36},
doi = {10.1007/s10922-025-10010-9},
url = {https://doi.org/10.1007/s10922-025-10010-9},
issn = {1573-7705},
}
@article{domurlsbert2024,
title={{DomURLs\_BERT}: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classification},
author={Abdelkader {El Mahdaouy} and Salima Lamsiyah and Meryem {Janati Idrissi} and Hamza Alami and Zakaria Yartaoui and Ismail Berrada},
journal={arXiv preprint arXiv:2409.09143},
year={2024},
eprint={2409.09143},
archivePrefix={arXiv},
primaryClass={cs.CR},
url={https://arxiv.org/abs/2409.09143},
}
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]