Machine Learning-Based Vulnerability Detection in Mobile Applications

This paper presents a machine learning-based framework for automated security vulnerability detection in mobile applications. The framework integrates deep learning methodologies with domain-specific security feature engineering to address the limitations of traditional manual assessment methods. Our approach analyzes ten critical security indicators including data storage practices, API interactions, authentication mechanisms, and runtime behaviors to generate quantitative vulnerability assessments. The framework employs a multi-layered neural network optimized for security classification, achieving strong performance with ROC-AUC of 0.89 and PR-AUC of 0.85 on a synthetically generated dataset reflecting real-world vulnerability distributions. The architecture incorporates dropout regularization, L2 weight control, and feature normalization for robust prediction capabilities. Key contributions include: (1) an integrated framework combining security feature engineering with deep learning for mobile vulnerability detection, (2) automated risk stratification based on model predictions and feature importance analysis using SHAP techniques, (3) demonstrated scalability for CI/CD pipeline integration, and (4) significant improvements in assessment speed and consistency compared to conventional testing approaches. The framework provides actionable insights for developers and security professionals, offering a transformative approach to mobile application security assessment throughout the software development lifecycle.