Phishsighter

Phishing URL Detection Using Ensemble Machine Learning Techniques

This project implements a robust phishing URL detection system leveraging ensemble machine learning techniques to enhance prediction accuracy and reliability. The system identifies phishing URLs based on features extracted from web links by combining models like Random Forest, Gradient Boosting, and AdaBoost. The project includes feature engineering, model training, and evaluation, and is designed to help protect users from online threats. It is implemented in Python, utilizing libraries such as Scikit-learn and Pandas, with a user-friendly interface for testing URLs.

Table of Contents

Installation Instructions
Usage Instructions
Compatibility
Building the Browser Extension
Help and support

Installation Instructions

  git clone http://github.com/kunley247/phishsighter
  cd phishsighter
  python -m env myvenv
  source ./myvenv/Script/activate
  pip install -r requirements.txt

Usage Instructions

For the Web App

  python phishsighter.py --web

For the Command Line Interface

  python phishsighter.py --url="https://example.com"

1. For Browser Extension and Notification

Compatibility

1. Make sure you are using Python 3.10.0 version in a Virtual Environment (env) to avoid unnecessary errors
2. The Browser extension was only tested in google chrome browser.

Building the Browser Extension

1. Step 1: Enable the Developer Mode and click on load unpacked (this is the extension folder in the cloned repositories "https://github.com/kunley247/phishsighter/tree/main/extension"). just select the folder the then the extension will be unpacked into your chrome browser.

1. Step 2: Pin the extension to your browser tools bar

Help and support

If you have any question feel free to reach out to me on my personal email: cafeat9ja@gmail.com