samsung-ai-challenge

AI Sentinel - Behavioral Analysis Application

A Flask-based web application for real-time behavioral pattern analysis and anomaly detection using AI models.

Features

• Mouse Dynamics Analysis: Captures and analyzes mouse movement patterns
• Typing Pattern Analysis: Analyzes typing speed and patterns with 250 WPM threshold
• Real-time Monitoring: Live behavioral pattern detection
• AI-powered Classification: Distinguishes between human and robotic behavior

Project Structure

ai-challenge/src/
├── app.py                 # Main Flask application
├── model_definition.py    # PyTorch model architecture
├── behavior_model.pth     # Trained mouse dynamics model
├── typing_model.pkl       # Trained typing pattern model
├── templates/             # HTML templates
│   ├── index.html         # Landing page
│   ├── main.html          # Command center
│   └── about.html         # About page
├── requirements.txt       # Python dependencies
└── run.py                # Application startup script

Installation & Setup

1. Navigate to the project directory:

   cd ai-challenge/src
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   python run.py
   

   Or alternatively:

   python app.py
   

Usage

1. Start the application using one of the methods above
2. Open your browser and go to http://localhost:5000
3. Navigate to the Command Center at http://localhost:5000/main
4. Test the features:
   • Mouse Dynamics: Move your mouse in the monitoring area
   • Typing Analysis: Enter text for pattern analysis

API Endpoints

• GET / - Landing page
• GET /main - Command center interface
• GET /about - About page
• POST /predict - Mouse dynamics prediction
• POST /predict_typing - Typing pattern prediction

Deployment

For deployment on platforms like Render:

1. Ensure all files are in the src/ directory
2. Set the build command: pip install -r requirements.txt
3. Set the start command: gunicorn app:app --bind 0.0.0.0:$PORT
4. The application will be available at the provided URL

Technical Details

• Framework: Flask 2.3.3
• ML Framework: PyTorch 2.1.0
• ML Library: Scikit-learn 1.3.0
• Typing Speed Threshold: 250 WPM (above this = ROBOT classification)
• Model Types:
  • Mouse Dynamics: Transformer-based neural network
  • Typing Patterns: TF-IDF + Machine Learning classifier

Troubleshooting

• Model Loading Issues: Ensure behavior_model.pth and typing_model.pkl are in the same directory as app.py
• Port Issues: The application runs on port 5000 by default
• Dependencies: Make sure all requirements are installed correctly

License

This project is part of the AI Sentinel behavioral analysis system.