📌 Introduction

This project focuses on machine learning with a Zero Trust Learning approach. The Zero Trust concept, commonly used in cybersecurity, is applied here to protect deep learning models against adversarial attacks.

🔍 What is Zero Trust Learning?

Zero Trust Learning is a security framework for machine learning that assumes no data, model, or process is trustworthy unless proven otherwise. This method ensures security at multiple levels, including data input, model optimization, and output control.

🚀 Key Features

✅ Utilizes Convolutional Neural Networks (CNNs) for data processing
✅ Implements Zero Trust security mechanisms to prevent adversarial attacks
✅ Analyzes the impact of adversarial attacks on deep learning models
✅ Evaluates model performance under different conditions

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🔧 Installation & Requirements

Dependencies

Libraries used in this project:
Install the required dependencies:

• NumPy
• Pandas
• Matplotlib
• Logging
• Hashlib
• TensorFlow

Run the Notebook

 `jupyter notebook zero-trust-learning-cnn.ipynb` 

📊 Results & Analysis

In this project, adversarial attacks were applied to CNN models, and their impact on model performance was analyzed. Additionally, Zero Trust strategies for mitigating these attacks were evaluated.

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• If you have suggestions for improving the project, please submit a Pull Request.
• To report issues, please open an Issue.

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