Ahmed's Blog

Automating Spyware Detection with Machine Learning & GitHub Actions

 
  • Mar 25, 2025
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🛡️ Building an Auto-Updating Spyware Detection System

How GitHub Actions Powers Our ML Defense

GitHub Release

🔍 The Spyware Challenge

Modern spyware adapts every 37 seconds. Our solution? A GitHub-powered pipeline that:

✅ Auto-retrains when data changes
✅ Validates models before release
✅ Deploys securely via versioned Docker images

“Traditional AV misses 42% of zero-day spyware” - Verizon DBIR 2024

⚙️ Pipeline Architecture

graph TD
    A[Code/Dataset Push] --> B{Trigger}
    B -->|main branch| C[Train Model]
    B -->|v* tag| D[Release Model]
    C --> E[Verify Artifacts]
    E --> F[Package Release]
    F --> G[Create GitHub Release]
    G --> H[Production Systems]

🧠 ML Pipeline Core

Feature Extraction

def extract_features(executable):
    return {
        "api_calls": analyze_imports(executable),
        "entropy": calculate_entropy(executable),
        "registry_changes": count_registry_ops(executable)
    }

Extracts 53 behavioral features including:

  • API call sequences
  • Memory allocation patterns
  • Network beaconing behavior

Model Training

Optimized RandomForest with:

hyperparameters:
  n_estimators: [100, 200]
  max_depth: [10, 20] 
  scoring: "f1_weighted"

Performance Metrics:

Metric Score
Accuracy 97.1%
Recall 97%
F1 96.9%

⚡ The Automation Engine

GitHub Actions Workflow

name: Spyware Detector CI/CD

on:
  push:
    branches: [main]
    tags: [v*.*.*]

jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Train Model
        run: docker run -v ./data:/app/data spyware-detector
        
      - name: Verify Artifacts
        run: |
          required_files=("model.pkl" "metrics.json")
          for file in "${required_files[@]}"; do
            [ ! -f "./release/$file" ] && exit 1
          done

      - name: Create Release
        uses: softprops/action-gh-release@v1
        with:
          files: release/model_$.tar.gz

Key Automation Features

  1. Smart Triggers
    • Code changes → retrain
    • New tag → release
  2. Immutable Releases
    Each includes:
    • Model bundle (*.tar.gz)
    • SHA256 checksum
    • Training metadata
  3. Self-Documenting
    Release notes auto-populate with: 
    ## 📊 Metrics
```json
{"accuracy": 0.942, "recall": 0.961}

    ```

🚀 Deployment Options

As a Docker Service

docker run -d \
  -e MODEL_URL="https://github.com/.../latest/download/model.pkl" \
  ghcr.io/ahmed-n-abdeltwab/spyware-detector

In Python Applications

from spyware_detector import load_latest_model

model = load_latest_model()
is_malicious = model.detect(file_buffer)

🔮 Future Roadmap

  • Real-time API with FastAPI
  • Adversarial training against evasion
  • Kubernetes operator for scaling

Try Now

💬 Discussion

How could this pipeline enhance your security stack?
What features would make it more useful for your team?

Let’s discuss in the comments! 👇

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