Phishing Detector – ML-Based Phishing Link Detection

Overview

Phishing Detector is a machine learning-powered web application designed to detect phishing links. Built using Django, the platform allows users to check URLs for potential phishing threats in real time. The ML model is trained on phishing datasets to distinguish between malicious and legitimate links effectively.

Features

• ML-Based Detection – Analyzes URLs to classify them as phishing or safe.
• User-Friendly Interface – Simple web UI for checking link security.
• Django-Powered Backend – Efficient and scalable deployment.
• Real-Time Analysis – Instant detection and classification of URLs.

Technologies Used

• Python – Core programming language
• Machine Learning – Model trained using phishing datasets
• Scikit-Learn – ML library for model training and evaluation
• Django – Web framework for backend implementation
• HTML/CSS – Frontend for the user interface

Installation & Setup

Follow these steps to set up and run the project locally:
Prerequisite: Ensure you have Python 3.11 installed on your system.

1. Clone the Repository

    git clone https://github.com/Suryatejaeasari/Phishing-Detector.git
    cd phishing-detector
   

2. Create a Virtual Environment (Optional but Recommended)

   py -3.11 -m venv venv
   source venv/bin/activate  # For macOS/Linux
   venv\Scripts\activate     # For Windows
   

3. Install Dependencies

    pip install -r requirements.txt
   

4. Apply Migrations

   python manage.py migrate
   

5. Start the Django Server

   python manage.py runserver
   

   The app will run on http://127.0.0.1:8000/

Usage

• Open the web app in your browser.
• Enter a URL in the provided field.
• Click Submit to analyze the link.
• The system will classify it as Safe or Phishing based on the ML model’s prediction.

Screenshots

Future Enhancements

• Improve model accuracy with advanced feature engineering.
• Integrate an API for real-time phishing detection.
• Deploy the model on a cloud platform for global accessibility.