This repository implements a communication system utilizing autoencoder models for secure communication in the Gaussian Wiretap Channel scenario. The system includes an encoder, Bob's decoder, and Eve's decoder trained to communicate securely over a noisy channel.

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    |   This project utilizes the Same-Size-K-Means repository for implementing k-means clustering in the security training phase.    |
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    |               You can use [https://github.com/ndanielsen/Same-Size-K-Means?tab=BSD-3-Clause-1-ov-file#readme]                   |
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    |                                   address to access the codes of this project.                                                  |
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• Introduction
• Code Structure
• Key Components
• Usage
• Included Files
• Analysis
• Core Idea
• Advantages and Applications
• Challenges and Considerations
• Future Perspectives and Outlook
• Example Usage
• References
• License

This repository implements a secure communication system based on deep neural networks for the Gaussian Wiretap Channel. The core idea revolves around utilizing autoencoder models to enhance communication security against eavesdropping attacks. The system comprises an encoder, Bob's decoder, and Eve's decoder trained to communicate securely over a noisy channel. The code provides functionalities for model setup, training, evaluation, and testing, including Bit Error Rate calculations for different Signal-to-Noise Ratios (SNR). Additionally, it incorporates k-means clustering for security training and evaluation, aiming to create a secure communication scheme.

• Initialization: The code sets up necessary libraries, constants, and definitions for the neural network models.

• Utility Functions and Layers: It defines utility functions, layers, and models for the encoder and decoders.

• Training Methods: Includes methods for training Bob and Eve's decoders, as well as for the security training phase using k-means clustering.

• Evaluation Functions: Functions to calculate Bit Error Rates for different Signal-to-Noise Ratios (SNR) and visualize results.

• Testing and Visualization: Functions for testing and visualization, such as plotting loss and encoding patterns.

• Encoder Model: A neural networksmodel responsible for encoding the input messages.

• Decoder Models: Separate models for Bob and Eve, decoding messages from the encoded representations.

• Training Process: Includes training procedures for Bob, Eve, and the security training phase using k-means clustering.

• Evaluation: Evaluates the performance of the models by calculating Bit Error Rates under various SNRs.

1. Setup Environment:

   • Ensure necessary libraries are installed (TensorFlow, NumPy, Matplotlib, etc.).
   • Clone the repository to your local machine.

2. Training:

   • Adjust hyperparameters such as epochs, batch size, and learning rate if needed.
   • Run training scripts for Bob's and Eve's decoders separately, ensuring proper convergence.

3. Security Training:

   • Initialize k-means clusteringfor security training using the provided function.
   • Train the secure model with adjusted parameters, incorporating k-means labels for enhanced security.

4. Evaluation:

   • Test the autoencoder models with normal data to assess baseline performance.
   • Generate coded labels using k-means clustering and evaluate the secure communication system.
   • Plot and analyze the Symbol Error Rate versus SNR for both traditional and secure setups.

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            |           # Example code snippet                         |
            |           from your_module import YourClass              |
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            |           # Initialize the class                         |
            |           your_instance = YourClass()                    |
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            |           # Perform some action                          |
            |           result = your_instance.some_action()           |
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• Article-Deep-Learning-for-the-Gaussian-Wiretap-Channel.ipynb: The main implementation code with Jupyter Notebook.
• Article-Deep-Learning-for-the-Gaussian-Wiretap-Channel.py: The main implementation code with Python.
• EqualGroupsKMeans.py: Code for equal groups k-means clustering.

Examine the performance metrics to gauge the effectiveness of the proposed secure communication scheme. Interpret the results in terms of communication security, Bit Error Rates, and potential vulnerabilities.

The primary concept revolves around leveraging deep neural networks to establish secure communication in the Gaussian Wiretap Channel. By employing autoencoder models, the system aims to ensure confidentiality and integrity in data transmission, mitigating eavesdropping risks posed by adversaries like Eve. Key steps include initial communication, decoding by Bob, eavesdropping attempt by Eve, security analysis, and performance evaluation.

• Enhanced security in wireless communications and sensitive communication networks.
• Utilization of deep learning technologies to develop advanced and intelligent security solutions.
• Mitigation of eavesdropping attacks and assurance of information security in sensitive communications.

• Implementation and training of deep models under real-world channel conditions and various SNR scenarios.
• Optimization of model parameters to optimize security performance and communication quality.
• Management and control of computational and temporal resources in processing large and complex datasets.

This work lays the groundwork for further research and development in secure communication systems utilizing deep learning methodologies.Future endeavors could focus on refining model architectures, exploring novel security mechanisms, and addressing emerging challenges in securing communication channels against evolving threats.

• Deep Learning for the Gaussian Wiretap Channel - Original Paper

• Same-Size-K-Means

BOOK

• Data And Computer Communications WilliamStallings: This book has been used to teach the concepts of communication systems.

This repository is licensed under the BSD-3-Clause License. See the LICENSE file for more details.