KeyDNA 🧬

KeyDNA is a modern Keystroke Dynamics Biometric Authentication System. It doesn't just look at what you type (your password/phrase), but how you type it. It analyzes your unique typing rhythm to detect imposters, even if they know your exact passphrase!

🧠 What is Keystroke Dynamics?

Keystroke dynamics is a behavioral biometric. Just like your fingerprint or DNA, the way you type on a keyboard is unique to you. Key factors include:

• Dwell Time: How long you hold down a specific key.
• Flight Time: The time it takes for your finger to travel from releasing one key to pressing the next.

This system captures these micro-second timings and creates a "Typing Profile" for your user account.

✨ Features

• 💻 Beautiful Hacker Aesthetic UI (Dark mode, neon green accents, monospace fonts)
• 📊 Real-time Rhythm Visualization using Chart.js
• 🔐 Dual Modes:
  • Enroll: Type the phrase 5 times to build your typing DNA profile.
  • Verify: Type it once to authenticate.
• 🐍 Lightweight Python Backend using Flask.
• 🤖 Custom ML Distance Algorithm to calculate similarity scores and confidence percentages.

🛠️ How It Works

1. Capture: The JavaScript frontend (app.js) listens for keydown and keyup events, recording the precise timestamp (in milliseconds) for each keystroke.
2. Feature Extraction: The Python backend (model.py) calculates arrays of Dwell Times and Flight Times.
3. Training: By typing the phrase 5 times, the system calculates the mathematical mean (average) of your timings to create a stable profile.
4. Verification: When you attempt to log in, your new timings are compared against your saved profile using Manhattan Distance. If the distance is below a certain threshold, you are authenticated!

🚀 Installation and Usage

1. Clone the repository (or navigate to the folder):

   cd KeyDNA
   

2. Install the requirements: Make sure you have Python installed, then run:

   pip install -r requirements.txt
   

3. Run the Flask application:

   python app.py
   

4. Open in Browser: Navigate to http://127.0.0.1:5000 in your web browser.

🔬 The Science Behind It

In model.py, we use simple but effective statistical methods.

• We calculate absolute differences between your current attempt and your baseline profile.
• Total Distance = Sum of Dwell Differences + Sum of Flight Differences.
• Confidence Score drops as the Total Distance increases.
• Turkish Note: Sistem basit bir uzaklık metriği (Manhattan distance) kullanarak yeni yazılan verinin orijinal profile ne kadar benzediğini matematiksel olarak hesaplar.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.