A Robust and Explainable Transformer-Based Framework for Phishing Email Detection

1. 1Build a transformer-based model using a library like Hugging Face's Transformers
2. 2Configure the model to handle contextual language understanding for phishing email detection
3. 3Apply explainability techniques to the model, such as feature importance or attention visualization
4. 4Test the model on a dataset of labeled phishing emails
5. 5Run the model on a stream of incoming emails to detect potential phishing attacks
6. 6Evaluate the model's performance using metrics like accuracy and F1-score

Data scientists and cybersecurity experts on a team benefit from this framework as it improves phishing detection accuracy and provides insights into the decision-making process, allowing for more effective collaboration and security measures

💡 Transformer-based models can be made more interpretable and effective in detecting phishing emails by incorporating explainability techniques

🚨 Detect phishing emails with a robust and explainable transformer-based framework! 🚨