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--- |
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license: apache-2.0 |
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datasets: |
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- Adnan-AI-Labs/CleanedBalancedPhishingUrls |
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language: |
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- en |
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base_model: |
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- distilbert/distilbert-base-uncased |
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tags: |
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- phishing_url |
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--- |
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# Model Card for DistilBERT-PhishGuard |
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## Model Overview |
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**URLShield-DistilBERT** is a phishing URL detection model based on DistilBERT, fine-tuned specifically for the task of identifying whether a URL is safe or phishing. This model is designed for real-time applications in web and email security, helping users identify malicious links. |
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## Intended Use |
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- **Use Cases**: URL classification for phishing detection in emails, websites, and chat applications. |
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- **Limitations**: This model may have reduced accuracy with non-English URLs or heavily obfuscated links. |
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- **Intended Users**: Security researchers, application developers, and cybersecurity engineers. |
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# Model Card for DistilBERT-PhishGuard |
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🔍 What Sets PhishGuard Apart? |
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High Accuracy 📈 – Achieved up to 99.6% accuracy and 0.997 AUC on validation datasets. |
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Optimized for Speed 🚀 – Leveraging a distilled transformer model for faster predictions without compromising accuracy. |
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Real-World Data 🌐 – Trained and evaluated on diverse phishing and safe URLs, ensuring robust performance across domains. |
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📊 Performance Metrics (Averaged Across Epochs) |
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Accuracy: 99.6% |
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AUC (Area Under Curve): 0.997 |
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Training Loss: 0.054 |
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Validation Loss: 0.047 |
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Markdown |
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## Support the Project |
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If you find this project useful, consider buying me a coffee to support further development! ☕️ |
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<a href="https://buymeacoffee.com/adnanailabs"> |
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<img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me a Coffee"> |
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</a> |
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## Usage |
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This model can be loaded and used with Hugging Face's `transformers` library: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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#Load the model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("your-username/DistilBERT-PhishGuard") |
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model = AutoModelForSequenceClassification.from_pretrained("your-username/DistilBERT-PhishGuard") |
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#Sample URL for classification |
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url = "http://example.com" |
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inputs = tokenizer(url, return_tensors="pt", truncation=True, max_length=256) |
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outputs = model(**inputs) |
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predictions = torch.argmax(outputs.logits, dim=-1) |
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print("Prediction:", "Phishing" if predictions.item() == 1 else "Safe") |
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``` |
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## Performance |
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The model achieves high accuracy across different chunks of training data, with performance metrics above 98% accuracy and an AUC close to or at 1.00 in later stages. This indicates robust and reliable phishing detection across varied datasets. |
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## Limitations and Biases |
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The model's performance may degrade on URLs containing obfuscated or novel phishing techniques. |
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It may be less effective on non-English URLs and may need further fine-tuning for different languages or domain-specific URLs. |
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### Contact and Support |
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For questions, improvements, or support, please contact us through the Hugging Face community or open an issue in the model repository. |
