Phishing Website Detection Content Based

Abstract

1. Introduction

End-to-end machine learning pipeline for detecting phishing websites using HTML content-based features. Covers data collection, feature engineering, model training, and evaluation across five classifiers.

2. Objective

This is an End-to-End Machine Learning Project which focuses on phishing websites to classify phishing and legitimate ones. Particularly, I focused on content-based features like html tag based features. You can find feature extraction, data collection, preparation process here. Also, building ML models, evaluating them are available here.

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

3. Key Features / Modules

• Presence and counts of HTML tags (, , , , etc.)
• External resource ratios (scripts, images, links)
• Form action attributes and submission targets
• Favicon and redirect indicators
• 30+ binary and quantitative features per page

4. Technology Stack

5. System Requirements

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

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/emre-kocyigit/phishing-website-detection-content-based.git
cd phishing-website-detection-content-based

git clone https://github.com/emre-kocyigit/phishing-website-detection-content-based.git
cd phishing-website-detection-content-based
pip install -r requirements.txt

python data_collector.py
python feature_extraction.py

python machine_learning.py

python app.py
# visit http://localhost:5000

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 emre-kocyigit 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.