Canstralian/cybersec-ml-pipeline

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

Usage Guide 📖

1. 1.Data Upload
2. 2.Support for CSV and JSON formats
3. 3.Automatic handling of large datasets using Dask

1. 1.Pipeline Configuration
2. 2.Choose preprocessing steps
3. 3.Configure model parameters
4. 4.Select features for training

1. 1.Model Training
2. 2.Interactive parameter tuning
3. 3.Real-time performance metrics
4. 4.Visual model evaluation

Local Development

1. 1.Clone the repository

git clone https://huggingface.co/spaces/Canstralian/cybersec-ml-pipeline
cd cybersec-ml-pipeline

1. 1.Install dependencies

pip install -r requirements.txt

1. 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