Machine Learning Techniques for Ensuring Security in Mobile Applications: A Comprehensive Study

Abstract

With the rapid proliferation of mobile applications, ensuring robust security against sophisticated threats like malware, phishing, and unauthorized access has become critical. Traditional signature-based security mechanisms often fall short in detecting zero-day attacks and evolving malware. This study investigates the potential of Machine Learning (ML) techniques to enhance mobile application security through dynamic and intelligent threat detection. It explores a wide range of ML models, including supervised, unsupervised, deep learning, and reinforcement learning, highlighting their strengths in anomaly detection, behavioral analysis, and malware classification. The paper emphasizes the importance of feature engineering, real-world data, and privacy-preserving methods such as federated learning. It also provides a comparative analysis of various ML approaches and their accuracy in detecting threats on Android and cross-platform environments. Despite challenges like adversarial attacks and the need for explainable models, ML-driven security frameworks present a promising path toward resilient and adaptive mobile cybersecurity solutions, paving the way for future advancements in secure digital ecosystems.