Finetuned Bert Phishing Site Classification

🚀 Model Card for Fine - tuned BERT - Base - Uncased on Phishing Site Classification

This model is a fine - tuned BERT - Base - Uncased designed for phishing site classification. It predicts whether a website is "Safe" or "Not Safe" based on text input, offering valuable assistance in enhancing online security.

🚀 Quick Start

You can load the fine - tuned model directly from the Hugging Face Hub:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the tokenizer and model from Hugging Face Hub
model_name = "shogun - the - great/finetuned - bert - phishing - site - classification"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example usage
text = "Enter your login credentials to claim a free reward!"
inputs = tokenizer(text, return_tensors="pt", truncation=True)
outputs = model(**inputs)

# Get the predicted label
logits = outputs.logits
prediction = logits.argmax(dim=-1).item()
print("Prediction:", "Not Safe" if prediction == 1 else "Safe")

✨ Features

Model Details

This model is a fine - tuned version of [BERT - Base - Uncased](https://huggingface.co/google - bert/bert - base - uncased) for phishing site classification. The model predicts whether a website is classified as "Safe" or "Not Safe" based on textual input.

Model Sources

• Dataset: [shawhin/phishing - site - classification](https://huggingface.co/datasets/shawhin/phishing - site - classification)

📚 Documentation

Uses

Direct Use

This model can be directly used for phishing detection by classifying text into two categories: "Safe" and "Not Safe." Typical use cases include:

• Integrating with browser extensions for real - time website classification.
• Analyzing textual data for phishing indicators.

Downstream Use

Users can fine - tune the model further for specific binary classification tasks or for datasets with similar domains.

Out - of - Scope Use

This model might not perform well for:

• Non - English text.
• Adversarial phishing attacks or heavily obfuscated text.
• Tasks unrelated to text - based classification.

Bias, Risks, and Limitations

Bias

The model's predictions are influenced by the dataset used during fine - tuning. If the training data contains biases, these may reflect in the predictions.

Risks

• False positives: Legitimate websites flagged as phishing.
• False negatives: Some phishing sites might not be detected.
• Potential vulnerabilities to adversarial examples.

Recommendations

⚠️ Important Note

Regularly update the dataset and model to stay aligned with emerging phishing patterns.

💡 Usage Tip

Use in combination with other security measures for robust phishing detection.

📄 License

This model is licensed under Apache - 2.0 (or specify your license).