Model Card for DistilBERT-PhishGuard

Model Overview

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.

Intended Use

Use Cases: URL classification for phishing detection in emails, websites, and chat applications.
Limitations: This model may have reduced accuracy with non-English URLs or heavily obfuscated links.
Intended Users: Security researchers, application developers, and cybersecurity engineers.

Model Details

Architecture: DistilBERT for Sequence Classification
Language: Primarily English
License: Apache License 2.0
Dataset: Trained on labeled phishing and safe URLs from public and proprietary sources.

Usage

This model can be loaded and used with Hugging Face's transformers library:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/DistilBERT-PhishGuard")
model = AutoModelForSequenceClassification.from_pretrained("your-username/DistilBERT-PhishGuard")

# Sample URL for classification
url = "http://example.com"
inputs = tokenizer(url, return_tensors="pt", truncation=True, max_length=256)
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=-1)
print("Prediction:", "Phishing" if predictions.item() == 1 else "Safe")

## Performance
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.

## Limitations and Biases
The model's performance may degrade on URLs containing obfuscated or novel phishing techniques.
It may be less effective on non-English URLs and may need further fine-tuning for different languages or domain-specific URLs.

### Contact and Support
For questions, improvements, or support, please contact us through the Hugging Face community or open an issue in the model repository.