A robust Python implementation of chaos-based encryption using the Lorenz system and Plain Convolution Encryption (PCE). This project implements novel cryptographic methods that leverage chaos theory for secure communication, offering both addition and convolution-based encryption variants.

• 🔐 Chaos-based encryption using Lorenz system synchronization
• 🔄 Plain Convolution Encryption (PCE) implementation
• 🛡️ Enhanced security against Fourier analysis attacks
• 📊 Comprehensive testing and validation suite
• 📈 Visualization tools for encryption analysis

This repository implements the encryption methods from the paper "Plain Convolution Encryption as an Alternative to Overcoming the Limitations of Synchronization-Based Methods", demonstrating both theoretical foundations and practical applications in secure communication.

Paper Reference:

• Title: Plain Convolution Encryption as an Alternative to Overcoming the Limitations of Synchronization-Based Methods
• Authors: Flavio Rosales-Infante, M.L. Romero-Amezcua, Iván Álvarez-Rios, F. S Guzmán
• arXiv Link: https://arxiv.org/abs/2504.03027v1

• Python (>= 3.10 recommended)
• NumPy
• SciPy
• pytest (for running tests)
• Matplotlib (optional, for visualizing results similar to the paper's figures)

A requirements.txt file is included for easy installation of dependencies.

1. Clone the repository:

   git clone https://github.com/ramsyana/lorenz-chaos-encrypt.git
   cd lorenz-chaos-encrypt

2. Create and activate a virtual environment (recommended):

   python -m venv venv
   # On Windows:
   # venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

This project uses pytest for unit testing. To run the tests:

1. Make sure you are in the root directory of the project (where the pytest.ini or pyproject.toml file would be, or just the main project folder).

2. Ensure your virtual environment is activated.

3. Run the following command:

   pytest

   Or for more detailed output:

   pytest -v

The tests cover individual functions within the modules (lorenz.py, messages.py, pce.py, hacking.py, etc.) to ensure correctness based on the paper's algorithms and expected numerical behavior.

This project is licensed under the MIT License.

MIT License

Copyright (c) 2025 [Ramsyana/ramsyana.com - ramsyana[at]mac.com]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

If you'd like to contribute:

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Ramsyana - ramsyana[at]mac.com

If you're interested in collaborating on the mathematical aspects of chaos theory, cryptography, or related mathematical research within this project, please don't hesitate to reach out. I welcome discussions on theoretical foundations, numerical methods, and mathematical optimizations.

chaos-theory cryptography lorenz-system encryption python mathematical-modeling secure-communication chaos-synchronization numerical-methods scientific-computing