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A newer version of the Streamlit SDK is available: 1.49.1

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metadata
title: ML Pipeline for Cybersecurity Purple Teaming
emoji: 🛡️
colorFrom: red
colorTo: blue
sdk: streamlit
sdk_version: 1.28.1
app_file: app.py
pinned: false
license: mit

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.

Open In Spaces

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. Visit the Space on Hugging Face Hub
  2. Upload your cybersecurity dataset (CSV/JSON format)
  3. Configure the ML pipeline parameters
  4. Train and evaluate your model
  5. Export the trained model for deployment

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

Local Development

  1. Clone the repository
git clone https://huggingface.co/spaces/Canstralian/cybersec-ml-pipeline
cd cybersec-ml-pipeline
  1. Install dependencies
pip install -r requirements.txt
  1. Run the application
streamlit run app.py

Contributing 🤝

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

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