🔐 Phishing URL Detection System

An AI-driven application designed to detect phishing URLs using machine learning. It features a Python-based Flask backend API and an interactive HTML/CSS/JavaScript frontend for real-time analysis. This project addresses the challenge of identifying deceptive URLs to protect users from cyber threats.

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🚀 Key Features

• Advanced AI Model – Uses an ensemble of ML models: Random Forest, XGBoost, LightGBM.
• Feature-Rich Analysis – Extracts and analyzes over 87 lexical and host-based features.
• RESTful Backend API – Flask-based API that serves the ML model for easy integration.
• Interactive Frontend – Modern interface (HTML/CSS/JS) for real-time URL safety checks.
• Fallback Mechanism – Includes a basic client-side analysis engine if the backend API is unavailable.
• Modular Codebase – Separate scripts for model training and backend server.

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🛠️ Tech Stack

• Backend: Python, Flask, Pandas, Scikit-learn, XGBoost, LightGBM, tldextract
• Frontend: HTML, CSS, JavaScript
• Dataset: dataset_phishing.csv

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📦 Prerequisites

• Python 3.7+
• pip (Python package manager)

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⚙️ Setup & Installation

1. Project Files

Ensure all files are in your project directory:

• phishing_dataset.py → Model training script
• flask_backend_api.py → Backend API server
• Phishing Detection App.html → Frontend application
• dataset_phishing.csv → Training dataset

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2. Create a Virtual Environment (Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

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3. Install Required Packages

pip install Flask Flask-Cors pandas numpy scikit-learn xgboost lightgbm tldextract

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▶️ Running the Application

Step 1: Train the Machine Learning Model

Generate the ML model (phishing_model_predefined.pkl):

python phishing_dataset.py

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Step 2: Start the Backend API Server

Run the Flask server (keep this terminal window open):

python flask_backend_api.py

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Step 3: Open the Frontend

Double-click:

Phishing Detection App.html

➡️ The status indicator will show “AI Model Connected” once the backend is running. You can now begin analyzing URLs.

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🌐 Browser Extension Integration

You can integrate the backend API with a Chrome/Edge extension for seamless real-time protection.

Installation (Chrome/Edge)

1. Open your browser’s extension management page:

   • Chrome: chrome://extensions/
   • Edge: edge://extensions/

2. Enable Developer mode (toggle switch).

3. Click Load unpacked.

4. Select the phishing-detector project folder.

5. The Phishing Detector extension will now appear in your extensions list.

   • Pin it to your toolbar for quick access.

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