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--- |
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library_name: transformers |
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tags: |
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- text-classification |
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- malicious-url-detection |
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--- |
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# Malicious-URL-Detector-v2 |
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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. |
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## Model Details |
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### Model Description |
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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. |
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- **Developed by:** Eason Liu |
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- **Language:** English |
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- **Model Type:** Text Classification (URL-focused) |
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- **Finetuned From:** [distilroberta-base](https://huggingface.co/distilroberta-base) |
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## Intended Use |
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### Direct Use |
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- **URL Classification:** Detect whether a URL is malicious (e.g., phishing, malware) or benign. |
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- **Security Pipelines:** Integrate into email filtering systems or website scanning tools to flag harmful links. |
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### Out-of-Scope Use |
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- **General Text Classification:** This model is specialized for URL data and may not perform well on arbitrary text inputs. |
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- **Advanced Contextual Analysis:** It does not consider broader context such as domain reputation or real-time link behavior. |
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## How to Get Started |
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Below is a quick example showing how to use this model with the 🤗 Transformers `pipeline`: |
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```python |
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from transformers import pipeline |
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# Initialize the text-classification pipeline with this fine-tuned model |
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classifier = pipeline( |
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"text-classification", |
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model="Eason918/malicious-url-detector-v2", |
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truncation=True |
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) |
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# Example URL to classify |
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url = "http://example.com/suspicious-link" |
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# Get the classification result |
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result = classifier(url) |
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print(result) |
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# Example output: [{'label': 'malicious', 'score': 0.9876}] |
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