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A machine learning-based framework for testing and assessing mobile application security vulnerabilities.
pip install api-security-tester
from api_security_tester import MobileAppSecurityFramework
# Initialize the framework
framework = MobileAppSecurityFramework()
# Example security features to analyze
app_features = {
'storage_encryption_level': 0.8,
'api_security_score': 0.7,
'data_transmission_security': 0.9,
'authentication_strength': 0.8,
'input_validation_score': 0.7,
'network_communication_security': 0.8,
'third_party_library_risk': 0.2,
'runtime_permissions_management': 0.7,
'code_obfuscation_level': 0.6,
'certificate_pinning_implementation': 0.8
}
# Detect vulnerabilities
results = framework.detect_vulnerabilities(app_features)
# Generate security report
report = framework.generate_security_report(results)
print(report)
The framework analyzes the following security aspects:
# Generate and train with custom dataset
framework.generate_dataset(n_samples=1000)
framework.load_dataset('mobile_app_vulnerabilities.csv')
framework.build_ml_model()
framework.train_model()
# Save trained model
framework.save_model('custom_model.h5')
# Get model performance metrics
metrics = framework.calculate_advanced_metrics()
print(f"ROC-AUC Score: {metrics['roc_auc']}")
print(f"PR-AUC Score: {metrics['pr_auc']}")
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
For support, please open an issue in the GitHub repository or contact contact@ashinno.com
FAQs
A machine learning based mobile app security testing framework
We found that api-security-tester demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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