Lumina Malware Detection API

Lumina is a machine learning-powered malware detection system that analyzes Windows PE files. It extracts 50+ static features and uses a LightGBM classifier to detect malware with 99.9% accuracy.

Model Architecture

Features

• Fast Analysis: < 2 seconds per file
• Static Analysis: No file execution required
• REST API: Easy integration
• Batch Processing: Analyze multiple files
• Detailed Results: Confidence scores & probability breakdowns

Quick Start

1. Clone & Setup

git clone https://github.com/IFTE-13/Lumina-API.git
cd Lumina-API

# Create virtual environment
python -m venv venv
venv\Scripts\activate  # Windows
# source venv/bin/activate  # Linux/Mac

# Install dependencies
pip install -r requirements.txt

2. Prepare Dataset

# Collect benign Windows executables
python setup_benign_data.py

# Download malicious dataset from:
# https://data.mendeley.com/datasets/vnj7sxkt53/1
# Place Header.csv in dataset/

# Build balanced dataset
python build_dataset.py

3. Train Model

python train_model.py

This creates:

• malware_model.pkl - Trained model
• scaler.pkl - Feature scaler
• feature_columns.pkl - Feature names

4. Run API Server

python app.py

Server runs at http://localhost:8000

API Endpoints

Method	Endpoint	Description
GET	/	API info
GET	/health	Health check
POST	/predict	Analyze .exe file

Test the API

# Health check
curl http://localhost:8000/health

# Analyze a file
curl -X POST http://localhost:8000/predict -F "file=@sample.exe"

Response Example

{
  "filename": "sample.exe",
  "verdict": "MALICIOUS",
  "confidence": 95.5,
  "probability_benign": 0.045,
  "probability_malicious": 0.955
}

Testing

# Test with EICAR (rename to .exe first)
curl -X POST http://localhost:8000/predict -F "file=@eicar.exe"

# Test with legitimate Windows executable
curl -X POST http://localhost:8000/predict -F "file=@notepad.exe"

Model Performance

Metric	Score
Accuracy	97.1%
Precision	95.4%
Recall	97.8%
F1 Score	97.1%

Requirements

• Python 3.10+
• 4GB RAM minimum
• Windows OS (for benign sample collection)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Credits

• PEFile - PE parsing
• LightGBM - ML framework
• Mendeley Dataset - Training data

Author

MOHAMMED IFTEKHAR

GitHub: @IFTE-13