At a Glance

Name: Build a Phishing Filter with Finetuned BERT and Granite
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: jianping_ye, joseph_santarcangelo, wojciech_fulmyk, matthew_wu

Build an interactive Gradio web app by fine-tuning BERT, DistilBERT, and IBM Granite LLM to detect phishing emails and strengthen AI-driven cybersecurity defenses. Use Huggingface pre-trained LLMs to fine-tune your own BERT-based phishing email classifier from data loading to model delivery. Integrate the IBM Granite API to design an LLM assistant that highlights malicious patterns, detects suspicious phrases, and reveals evolving attack strategies. By the end, you will have created a shareable web app with Gradio that deploys your fine-tuned model and LLM assistant to protect users worldwide.

Have you been annoyed and scared by phishing emails? Phishing is one of the most common and dangerous cyberattacks, designed to trick users into giving up sensitive information or clicking malicious links. In this project, you’ll build an AI-powered phishing detection system that goes beyond traditional spam filters. By fine-tuning transformer models like DistilBERT and integrating IBM’s Granite as an explanation layer, you’ll create a two-step defense that not only classifies emails as legitimate or suspicious but also selects and explains the exact words and phrases that make them dangerous. Along the way, you’ll gain hands-on experience with Hugging Face fine-tuning, explainable AI, and deploying interactive web apps with Gradio, which are skills directly applicable to real-world cybersecurity challenges.

What You’ll Learn

By the end of this project, you will be able to:

Fine-tune BERT-based models for phishing detection: Train and evaluate lightweight transformers to classify emails as legitimate or phishing.
Integrate LLMs for explainability: Use IBM Granite as a second defense layer to extract and explain suspicious phrases that indicate phishing attempts.
Build an interactive web interface: Develop a Gradio-powered demo where users can paste emails, get predictions, view confidence scores, and read explanations.
Apply explainable AI in cybersecurity: Learn how combining classifiers with reasoning-capable LLMs improves both accuracy and transparency.

Who Should Enroll

Early-career data scientists or ML engineers who want to apply NLP to cybersecurity problems.
Cybersecurity enthusiasts and professionals looking to understand how AI can enhance phishing detection.
Students and researchers interested in explainable AI and real-world model deployment.

Why Enroll

This project bridges machine learning and cybersecurity by showing how explainable AI can strengthen defenses against phishing. Instead of just flagging emails as "safe" or "suspicious", your system will provide transparent reasoning that helps users understand and trust the model’s predictions. By the end, you’ll have a working phishing detection assistant, practical experience with Hugging Face fine-tuning, and a deeper understanding of how LLMs can be used as a second layer of defense in AI security systems.

What You’ll Need

To get the most out of this project, you should have:

Basic Python programming knowledge.
Some familiarity with NLP or Hugging Face models (helpful but not required).
Interest in practical AI applications.

All dependencies are pre-configured in the environment, and the project runs best on current versions of Chrome, Edge, Firefox, or Safari.

Offered By: IND

Build a Phishing Filter with Finetuned BERT and Granite

At a Glance

What You’ll Learn

Who Should Enroll

Why Enroll

What You’ll Need