ML Pipeline for Cybersecurity Purple Teaming 🛡️

A scalable Streamlit-based machine learning pipeline platform specialized for cybersecurity purple-teaming, enabling advanced data processing and model training.

Features 🚀

• Distributed Data Processing: Leverage Dask for handling large-scale datasets
• Interactive ML Pipeline: Build and customize machine learning workflows
• Real-time Visualization: Monitor model performance and data insights
• Cybersecurity Focus: Tailored for purple team operations and security analytics

Tech Stack 💻

• Dask: Distributed data processing
• Scikit-learn: ML model training and evaluation
• Streamlit: Interactive web interface
• Pandas/NumPy: Data manipulation and analysis
• Matplotlib/Seaborn: Data visualization

Getting Started 🏁

1. Clone the repository

git clone https://github.com/yourusername/cybersec-ml-pipeline.git
cd cybersec-ml-pipeline

2. Install dependencies

pip install -r requirements.txt

3. Run the application

streamlit run app.py

Usage Guide 📖

1. Data Upload

   • Support for CSV and JSON formats
   • Automatic handling of large datasets using Dask

2. Pipeline Configuration

   • Choose preprocessing steps
   • Configure model parameters
   • Select features for training

3. Model Training

   • Interactive parameter tuning
   • Real-time performance metrics
   • Visual model evaluation

GitHub Actions for Hugging Face Hub

To set up GitHub Actions for pushing to Hugging Face Hub, follow these steps:

1. Create a GitHub Actions workflow file: The workflow file should be located at .github/workflows/hf-push.yml.

2. Trigger the workflow: The workflow should be triggered on a push to the main branch.

3. Set up Python environment: Ensure the Python version is set to 3.11.

4. Install dependencies: Install the necessary dependencies including requests, pandas, numpy, plotly, scikit-learn, statsmodels, streamlit, nltk, and huggingface_hub.

5. Retrieve Hugging Face token: The Hugging Face token (HF_TOKEN) should be retrieved from the GitHub secrets and set as an environment variable.

6. Push to Hugging Face Hub: Use the huggingface_hub library to push the repository contents to the Hugging Face Hub.

Make sure you have the HF_TOKEN secret set up in your GitHub repository settings to authenticate with Hugging Face Hub.

Contributing 🤝

Please read our Contributing Guidelines for details on our code of conduct and the process for submitting pull requests.

Security 🔒

For security concerns, please review our Security Policy.

License 📄

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments 👏

• Streamlit community for the amazing framework
• Scikit-learn team for the ML tools
• All contributors who help improve this project