Exploring Neural Networks for Encryption and Decryption: A Cryptanalysis Perspective

An AI-driven encryption algorithm leveraging neural networks as adaptive alternatives to traditional encryption methods like AES and RSA. The system utilizes an encoder to transform plaintext into ciphertext and a decoder to revert ciphertext to its original form, offering enhanced security and resistance to unauthorized access.

🔥 Features

• 🔒 AI-based Encryption & Decryption using TensorFlow & Keras
• 🧠 Neural Network Architecture with Dense Layers & Leaky ReLU
• 🛡 Robust Security against cryptanalysis and adversarial attacks
• ☁️ Scalable for secure cloud storage and real-time communications
• ⚡ Outperforms traditional encryption methods in cryptanalysis

🛠 Tech Stack

• Frameworks: TensorFlow, Keras
• Languages: Python
• Activation Function: Leaky ReLU
• Tools: NumPy, Pandas

📂 Project Structure

├── LICENSE
├── __init__.py
├── config.py             # Configuration settings
├── de1.py                # Decryption logic
├── en1.py                # Encryption logic
├── encryption_large.py   # Large model for encryption
├── encryption_small.py   # Small model for encryption
├── requirements.txt      # Python dependencies
└── utils.py              # Utility functions

🚀 Getting Started

Clone the repo:

git clone https://github.com/nithins7676/neural_encryption_networks.git
cd neural_encryption_networks

Install dependencies:

pip install -r requirements.txt

Run:

python en1.py
python de1.py

python encryption_large.py
python encryption_small.py

📈 Results

The neural network-based encryption demonstrates enhanced security, outperforming traditional methods in cryptanalysis tests and showcasing adaptability against evolving cyber threats.

🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

📄 License

This project is licensed under the MIT License.