Phishytics Machine Learning For Phishing

Abstract

1. Introduction

Machine Learning and Random Forests with Byte Pair Encoding and TFIDF scores for Phishing Website Detection.

2. Objective

Machine Learning for Phishing Website Detection

This project demonstrates how HTML, Machine Learning can be applied to a real-world Cybersecurity & Ethical Hacking problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• A modern web browser
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/faizann24/phishytics-machine-learning-for-phishing.git
cd phishytics-machine-learning-for-phishing

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add logging and alert notifications (email / Telegram)
• Write a threat model document for the tool
• Package it with Docker for safe lab testing

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which threat or attack does this project defend against?
2. What detection or protection technique is used and what are its limits?
3. How are false positives and false negatives handled?
4. Which cryptographic algorithms or security standards are involved?
5. What legal and ethical rules apply when testing a tool like this?

9. Source Code & License

This project is developed by faizann24 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.