Spyware Detection Training Pipeline

A containerized pipeline for training and deploying spyware detection models.

Features

• 🐳 Production-ready Docker container
• 🧩 Modular component architecture
• 📊 Comprehensive model evaluation
• 🔐 Secure non-root execution
• 📦 Optimized multi-stage build

Quick Start

Prerequisites

• Docker 20.10+
• Python 3.9+

Using Docker (Recommended)

1. Build the image:

   docker build -t spyware-detector .

2. Run training:

   docker run --rm \
     -v $(pwd)/data:/app/data \
     -v $(pwd)/models:/app/models \
     -v $(pwd)/release:/app/release \
     spyware-detector

Local Development

1. Setup environment:

   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

2. Run pipeline:

   python src/main.py

Configuration

Edit YAML files in config/ to customize:

# Example config/components/model_trainer.yaml
params:
  model_type: "random_forest"
  hyperparams:
    n_estimators: [100, 200]
    max_depth: [10, 20]

Project Structure

.
├── config/           # Pipeline configuration
├── data/             # Training data
├── models/           # Trained models
├── release/          # Deployment packages
├── src/              # Application code
└── tests/            # Unit tests

CI/CD Integration

The included GitHub workflow:

1. Builds Docker image on push
2. Runs training pipeline
3. Packages artifacts
4. Creates GitHub release

Security Best Practices

• Non-root container user
• Minimal runtime image
• Regular dependency updates
• Isolated build environment

License

MIT License - See LICENSE for details.