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---
library_name: transformers
tags:
- text-classification
- malicious-url-detection
---
# Malicious-URL-Detector-v2
Leveraging this fine-tuned model, you can identify harmful links intended to exploit users—such as phishing or malware URLs—by accurately classifying them as either malicious or benign.
## Model Details
### Model Description
This model is a **fine-tuned** version of [distilroberta-base](https://huggingface.co/distilroberta-base), adapted specifically for malicious URL detection. It employs a text-classification approach to distinguish between benign and malicious URLs. By learning patterns from a curated dataset of phishing, malware, and legitimate URLs, the model helps users and organizations enhance their defenses against a wide range of cyber threats.
- **Developed by:** Eason Liu
- **Language:** English
- **Model Type:** Text Classification (URL-focused)
- **Finetuned From:** [distilroberta-base](https://huggingface.co/distilroberta-base)
## Intended Use
### Direct Use
- **URL Classification:** Detect whether a URL is malicious (e.g., phishing, malware) or benign.
- **Security Pipelines:** Integrate into email filtering systems or website scanning tools to flag harmful links.
### Out-of-Scope Use
- **General Text Classification:** This model is specialized for URL data and may not perform well on arbitrary text inputs.
- **Advanced Contextual Analysis:** It does not consider broader context such as domain reputation or real-time link behavior.
## How to Get Started
Below is a quick example showing how to use this model with the 🤗 Transformers `pipeline`:
```python
from transformers import pipeline
# Initialize the text-classification pipeline with this fine-tuned model
classifier = pipeline(
"text-classification",
model="Eason918/malicious-url-detector-v2",
truncation=True
)
# Example URL to classify
url = "http://example.com/suspicious-link"
# Get the classification result
result = classifier(url)
print(result)
# Example output: [{'label': 'malicious', 'score': 0.9876}]
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