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--- |
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title: ML Pipeline for Cybersecurity Purple Teaming |
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emoji: 🛡️ |
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colorFrom: red |
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colorTo: blue |
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sdk: streamlit |
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sdk_version: 1.28.1 |
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app_file: app.py |
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pinned: false |
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license: mit |
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--- |
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# ML Pipeline for Cybersecurity Purple Teaming 🛡️ |
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A scalable Streamlit-based machine learning pipeline platform specialized for cybersecurity purple-teaming, enabling advanced data processing and model training. |
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[](https://huggingface.co/spaces/Canstralian/cybersec-ml-pipeline) |
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## Features 🚀 |
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- **Distributed Data Processing**: Leverage Dask for handling large-scale datasets |
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- **Interactive ML Pipeline**: Build and customize machine learning workflows |
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- **Real-time Visualization**: Monitor model performance and data insights |
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- **Cybersecurity Focus**: Tailored for purple team operations and security analytics |
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## Tech Stack 💻 |
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- **Dask**: Distributed data processing |
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- **Scikit-learn**: ML model training and evaluation |
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- **Streamlit**: Interactive web interface |
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- **Pandas/NumPy**: Data manipulation and analysis |
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- **Matplotlib/Seaborn**: Data visualization |
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## Getting Started 🏁 |
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1. Visit the [Space on Hugging Face Hub](https://huggingface.co/spaces/Canstralian/cybersec-ml-pipeline) |
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2. Upload your cybersecurity dataset (CSV/JSON format) |
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3. Configure the ML pipeline parameters |
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4. Train and evaluate your model |
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5. Export the trained model for deployment |
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## Usage Guide 📖 |
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1. **Data Upload** |
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- Support for CSV and JSON formats |
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- Automatic handling of large datasets using Dask |
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2. **Pipeline Configuration** |
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- Choose preprocessing steps |
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- Configure model parameters |
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- Select features for training |
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3. **Model Training** |
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- Interactive parameter tuning |
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- Real-time performance metrics |
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- Visual model evaluation |
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## Local Development |
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1. **Clone the repository** |
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```bash |
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git clone https://huggingface.co/spaces/Canstralian/cybersec-ml-pipeline |
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cd cybersec-ml-pipeline |
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``` |
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2. **Install dependencies** |
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```bash |
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pip install -r requirements.txt |
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``` |
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3. **Run the application** |
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```bash |
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streamlit run app.py |
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``` |
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## Contributing 🤝 |
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Please read our [Contributing Guidelines](CONTRIBUTING.md) for details on our code of conduct and the process for submitting pull requests. |
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## License 📄 |
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
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## Acknowledgments 👏 |
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- Streamlit community for the amazing framework |
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- Scikit-learn team for the ML tools |
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- All contributors who help improve this project |
