🔒Phishing URL Detection

Overview

This project demonstrates a phishing detection system using a trained machine learning model served via a Flask web application. Users can input a URL and get real-time predictions of whether it's legitimate or phishing, along with confidence percentages.

Live Demo

Access the live app here:
Phishing URL Detector
( Note: It may take upto a minute to load for the first time)

Key Features

• Trained scikit-learn pipeline with TF-IDF (word and character level) and LinearSVC
• Calibrated model outputs for accurate probability estimates
• Flask web app to interact with the model via a browser
• Dynamic visual feedback using progress bars for prediction confidence
• Render or GitHub-compatible for deployment

Dataset and Model

• Based on the following dataset of URLs labeled as legitimate or phishing:
  • https://huggingface.co/datasets/pirocheto/phishing-url
• Pipeline includes:
  • TF-IDF feature extraction (word and character)
  • Linear Support Vector Machine with calibration for probabilities
• Model is saved as a pickle file and loaded at runtime

Project Structure

phishing-url-detector/
├── app.py             # Main Flask server
├── templates/
│ └── index.html       # Frontend HTML
├── model/
│ └── model.pkl        # Trained machine learning model
├── helpers.py         # Model training and utilities
├── requirements.txt   # Python dependencies
└── README.md          # Project documentation

Getting Started

Prerequisites

• Python 3.8 or higher
• pip for dependency installation

Installation and Running

Clone the repository and install dependencies:

git clone https://github.com/yourusername/phishing-url-detector.git
cd phishing-url-detector
pip install -r requirements.txt

Run the web app:

python app.py

Then open http://127.0.0.1:5000 in your browser.

License

This project is licensed under the MIT License.

Contact

Contributions and feedback are welcome. Feel free to reach out or open an issue on GitHub.