Our Products

product

Features of this Advanced Phishing Detection System: 1. Modern GUI with Tkinter

  • Clean, professional interface with dark theme

  • Tabbed navigation for different functionalities

  • Progress indicators and visual feedback

  • Responsive design with scrollable areas

2. Machine Learning Integration

  • Random Forest classifier for phishing detection

  • Feature extraction from URLs (length, special characters, HTTPS, etc.)

  • Confidence scoring and probability distribution

  • Demo model with synthetic data (trainable on real data)

3. Core Functionalities

  • Single URL Analysis: Real-time phishing detection for individual URLs

  • Batch Analysis: Process multiple URLs at once

  • History Tracking: Maintain analysis history with timestamps

  • Model Information: Display details about the ML model

4. Advanced Features

  • Visual confidence meters and progress bars

  • Color-coded risk assessment (Red for phishing, Green for safe)

  • Feature analysis display showing extracted URL characteristics

  • Probability distribution visualization

5. User Experience

  • Threaded operations to prevent UI freezing

  • Status bar for real-time feedback

  • Error handling with user-friendly messages

  • Clear and intuitive controls

Installation Requirements:

Create a requirements.txt file:

text

scikit-learn==1.3.0 pandas==2.0.3 numpy==1.24.3 joblib==1.3.2 requests==2.31.0 python-whois==0.9.3 Usage Instructions:

  1. Install dependencies:

bash

pip install -r requirements.txt

  1. Run the application:

bash

python phishing_detector.py

  1. To test:

    • Enter URLs in the "URL Analysis" tab

    • Use "Batch Analysis" for multiple URLs

    • Check "Model Info" for technical details

    • View "History" for past analyses

Sample Test URLs:

Try these for testing:

  • https://www.google.com (Legitimate)

  • http://secure-login-verify-account.com (Suspicious)

  • http://192.168.1.1/login.php (Suspicious - IP address)

  • https://www.paypal.com.secure.login.verify.account.com (Phishing attempt)

Note:

This is a demonstration system using synthetic data. For production use:

  1. Train on real phishing datasets (like PhishTank)

  2. Implement more sophisticated feature extraction

  3. Add real-time WHOIS lookup

  4. Include content analysis

  5. Implement regular model updates

Comments

Leave a Comment

Comment*

Reviews

Write Your Reviews

(0.0)

comment*

Up to Top