README.md

---
library_name: transformers
tags:
  - text-classification
  - malicious-url-detection
---

# Malicious-Url-Detector

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 [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased), 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, it aims to help users and organizations bolster their defenses against a range of cyber threats.

- **Developed by:** Eason Liu  
- **Language:** English  
- **Model Type:** Text Classification (URL-focused)  
- **Finetuned From:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)

## 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 tasks not related to malicious URL detection.
- Tasks requiring more nuanced context beyond the URL string (e.g., domain reputation, 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",
    truncation=True
)

# Example URL
url = "http://example.com/suspicious-link"

# Classify the URL
result = classifier(url)
print(result)