Phishing Website Detection Application Using URL Features And Machine Learning

Abstract

1. Introduction

This is the dataset required to train the classifier

This is the python program to extract required columns from dataset and generate another csv file. The generated dataset is phishing_dataset_final.csv

This python file extracts only Phishy URLs from dataset in order to create a mongoDB database

2. Objective

Phishing website detection using URL features, blacklist and machine learning approach. Utilizes Flask, MongoDB and Random Forest classifier.

This project demonstrates how Python, Flask, 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.

• 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/varshini-as/Phishing-Website-Detection-Application-Using-URL-Features-and-Machine-Learning.git
cd Phishing-Website-Detection-Application-Using-URL-Features-and-Machine-Learning

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 varshini-as 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.