Entropy-Based Random Number Generator

A cryptographically secure random number generator that uses multiple entropy sources to generate high-quality random numbers between 0 and 2047.

Features

• Multiple Entropy Sources:

  • Mouse movement patterns and timing
  • CPU scheduler tasks and process information
  • Microphone audio input (GUI version only)

• Cryptographic Strength: Uses SHA-256 hashing to generate random numbers from collected entropy

• Two Versions Available:

  • Command-line interface (CLI)
  • Graphical user interface (GUI) with real-time status display

• Quality Assurance: Includes statistical randomness tests to verify output quality

• Portable Executables: Build standalone .exe files with PyInstaller

Requirements

For CLI Version

Python 3.7+
psutil>=5.9.0
pynput>=1.7.6

For GUI Version

Python 3.7+
psutil>=5.9.0
pynput>=1.7.6
numpy>=1.21.0
pyaudio>=0.2.11
tkinter (usually included with Python)

Installation

1. Clone the repository:

git clone <repository-url>
cd RNG

2. Install dependencies:

For CLI version:

pip install -r requirements.txt

For GUI version:

pip install -r requirements_gui.txt

Usage

Command-Line Interface

Run the CLI version:

python entropy_rng.py

Instructions:

1. The program will start collecting entropy automatically
2. Move your mouse continuously and randomly for 30 seconds
3. The countdown pauses if you stop moving the mouse
4. After collecting sufficient entropy, 10 random numbers will be generated
5. Enter interactive mode to generate more numbers on demand

Graphical User Interface

Run the GUI version:

python entropy_rng_gui.py

Instructions:

1. Click "Start Entropy Collection"
2. Move your mouse continuously for 30 seconds
3. Make noise near your microphone to add audio entropy
4. Watch the real-time status display showing:
   • Mouse activity status (ACTIVE/INACTIVE)
   • Accumulated active movement time
   • Sample counts from each entropy source
5. Once sufficient entropy is collected, click "Generate 24 Numbers"
6. View the generated numbers in a formatted grid display

How It Works

Entropy Collection

The RNG collects entropy from multiple unpredictable sources:

1. Mouse Movement Entropy:

   • Captures X/Y coordinates
   • Records high-precision timestamps (nanosecond accuracy)
   • Calculates timing deltas between movements
   • Combines all data using XOR operations

2. CPU Scheduler Entropy:

   • Samples CPU times (user, system, idle)
   • Counts active processes
   • Combines with high-precision timestamps
   • Collected every 10ms

3. Audio Entropy (GUI only):

   • Records microphone input
   • Analyzes RMS (Root Mean Square) values
   • Extracts peak amplitudes
   • Computes variance of audio samples
   • Combines with timestamps for additional randomness

Random Number Generation

1. Collects minimum required entropy samples (300+ mouse, 100+ audio)
2. Ensures at least 30 seconds of active mouse movement
3. Combines all entropy sources into a single pool
4. Uses SHA-256 cryptographic hash function to mix entropy
5. Converts hash output to numbers in range 0-2047
6. Each number uses unique combination of entropy data

Security Features

• Time-based uniqueness: Each generation includes nanosecond-precision timestamps
• Hash mixing: SHA-256 ensures avalanche effect (small input changes completely change output)
• No reuse: Entropy pool continuously grows during collection
• Multiple sources: Reduces dependence on any single entropy source

Testing Randomness Quality

Run the statistical analysis:

python test_randomness.py

The test suite includes:

• Frequency Test: Chi-square test for uniform distribution
• Runs Test: Checks for sequential patterns
• Serial Correlation: Detects correlation between consecutive numbers
• Gap Test: Analyzes gaps between value occurrences
• Shannon Entropy: Measures information content

Building Executables

Build standalone executables using PyInstaller:

For CLI version:

pyinstaller entropy_rng.spec

For GUI version:

pyinstaller entropy_rng_gui.spec

Executables will be created in the dist/ directory.

Project Structure

RNG/
├── entropy_rng.py           # CLI version
├── entropy_rng_gui.py       # GUI version with audio entropy
├── test_entropy_rng.py      # Simple test script
├── test_randomness.py       # Statistical randomness tests
├── requirements.txt         # CLI dependencies
├── requirements_gui.txt     # GUI dependencies
├── entropy_rng.spec         # PyInstaller spec for CLI
├── entropy_rng_gui.spec     # PyInstaller spec for GUI
└── README.md               # This file

Technical Details

• Output Range: 0 to 2047 (11 bits of entropy per number)
• Entropy Pool Size: Up to 2000 samples per source (configurable)
• Collection Rate: ~100 Hz for mouse movements, ~100 Hz for CPU, ~43 Hz for audio
• Hash Algorithm: SHA-256 (256-bit output)
• Mouse Activity Timeout: 2.0 seconds of inactivity before pausing

Limitations

• Requires user interaction (mouse movement) for entropy collection
• Audio entropy requires working microphone (GUI version)
• Not suitable for generating large volumes of random numbers quickly
• Best used when high-quality entropy is more important than generation speed

Use Cases

• Cryptographic key generation
• Secure password generation
• Lottery number selection
• Gaming random events requiring provable fairness
• Educational purposes to understand entropy-based RNGs
• Situations requiring verifiable randomness sources

Security Considerations

• Mouse movement patterns can be influenced by user habits
• CPU scheduler entropy may be predictable on idle systems
• Best results when multiple entropy sources are used together
• Not a replacement for OS-level cryptographic RNGs for production systems
• Suitable for scenarios where entropy source transparency is important

License

[Specify your license here]

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.

Acknowledgments

• Uses psutil for CPU monitoring
• Uses pynput for mouse event capture
• Uses pyaudio and numpy for audio processing
• Inspired by hardware random number generators and entropy collection techniques