Android Malware Detection using Machine Learning

Abstract : The rapid growth of Android smartphones has led to an exponential increase in the distribution and usage of Android applications. However, this popularity has also attracted cybercriminals who exploit the platform by developing malware to compromise user data, privacy, and device security. Traditional signature-based malware detection techniques are often ineffective against new and evolving threats, necessitating more advanced methods. Machine learning (ML) has emerged as a promising solution to enhance Android malware detection by leveraging data-driven algorithms that can identify malicious behavior patterns and anomalies. This study explores the application of various machine learning models, such as decision trees, support vector machines, random forests, and neural networks, for detecting malware on Android devices. Features extracted from application metadata, permissions, API calls, and behavioral characteristics are used to train and evaluate these models. The results demonstrate that machine learning approaches significantly improve detection accuracy, reduce false positives, and can adapt to novel malware variants through continuous learning. Furthermore, the integration of ML-based detection systems into Android security frameworks provides real-time protection and automated threat analysis. Challenges such as feature selection, data imbalance, and resource constraints on mobile devices are also discussed. This research highlights the potential of machine learning as an effective tool in combating Android malware, promoting safer mobile environments for users and developers alike.

Keyword Android malware detection, machine learning, mobile security, anomaly detection, application permissions, feature extraction, classification algorithms, real-time threat analysis, mobile device security, malware classification.