Project Proposal Feedback

[pooyanjamshidi]

Feedback on Phishing Detection Proposal:

First off, nice job laying out the problem and your solution. Phishing is a huge and growing issue, so the need for real-time detection is very clear. Your approach using a multimodal model and focusing on real-time classification is practical and timely. Below are some of my thoughts and suggestions that could help strengthen your proposal:

Strengths:

• Clear Problem Definition: The problem you’ve identified is well-documented, and phishing is one of those issues that is both technically challenging and of high impact. The real-time element of your solution is especially crucial since phishing attacks need to be mitigated instantly to prevent damage.
• Good Use of Pretrained Models: Fine-tuning pretrained models like BERT and GPT for phishing detection is a strong idea. Leveraging both text and metadata features adds robustness to your approach.
• Focus on Low-Latency: Focusing on the GRU model to minimize latency is a good decision. Since you’re aiming for real-time classification, keeping the model lightweight without sacrificing accuracy is key.
• Well-Considered Evaluation: Using F1 score and comparing your model to baseline models like BERT and GPT-2 is a solid plan. You’ve also mentioned human judgment and non-LLM solutions, which shows you’re thinking broadly about how to validate your model’s effectiveness.

Suggestions:

1. Active Prevention vs. Detection:

   • You mention in the feedback section that you’re thinking about allowing the model to actively prevent users from clicking on phishing links. This is a great idea, but implementing that feature adds complexity. You’ll need to think about how this would work within the email application—would it block the link entirely, give a warning, or just flag the email? Make sure to balance between user experience and security—users might get frustrated if they feel too restricted.

2. Accuracy Thresholds:

   • You’ve mentioned the accuracy thresholds for LSTM (96.9%), Bi-LSTM (99%), and GRU (97.5%), which is helpful. But for real-time systems, you’ll also want to define some operational thresholds beyond accuracy. For example, how tolerant can the system be of false positives and false negatives? Users won’t be happy if they lose legitimate emails, and the cost of false negatives (missing a phishing email) is also high. Consider balancing accuracy with precision/recall to minimize the trade-offs.

3. Comparison to Non-LLM Solutions:

   • It’s great that you’re thinking about comparing your model with non-LLM solutions. Make sure to explain what these non-LLM solutions are and how your approach improves upon them. It might be worth exploring other simpler but effective techniques like rule-based systems, regular expressions, or other classical machine learning methods like decision trees or SVMs for comparison.

4. Handling Concept Drift:

   • Phishing techniques evolve quickly, and what works today might not work tomorrow. You may want to mention how your model will handle this, especially since phishing URLs and techniques change. A section on how you plan to keep the model up-to-date (e.g., continuous learning or periodic re-training) would strengthen your proposal.

5. User Experience and Integration:

   • You’ve mentioned integrating the model into an email platform, which is great. However, you might want to elaborate a bit more on how this will work from a user’s perspective. Will users get an alert when an email is flagged? Can they override the system if they think it’s wrong? The smoother the integration, the better the user experience.

6. Real-Time Evaluation:

   • Your idea of testing the model on real-time email streams is excellent. I’d suggest expanding a bit on how you’ll set up this testing environment. Are you going to simulate live email traffic or use a sandbox environment? If possible, plan to show how well the model scales under load (number of emails per second it can process) and its latency.

Additional Considerations:

• Multi-Modal Model Enhancements:

  • You’re combining text-based transformers with URL features and metadata, which should help improve phishing detection accuracy. You might want to provide a bit more detail on how you’ll combine these features. Will they be combined early in the model, or will you use separate models for text and metadata, then merge their outputs? Some clarification would help.

• Error Analysis:

  • After evaluating your model, make sure to conduct a detailed error analysis. Understanding why the model misclassifies certain emails will be crucial for improvement. You can include this as part of your evaluation.

Conclusion:

Overall, this is a strong proposal. You’ve identified a real-world problem, chosen a practical approach (focusing on GRU for low latency), and thought about solid evaluation metrics. Just make sure to dive deeper into the user experience and how you’ll handle evolving phishing techniques (concept drift). Also, think about balancing detection with prevention and providing clear explanations of how your model improves upon existing solutions.

Looking forward to seeing how this evolves!