Effective ML-Based Android Malware Detection and Categorization

The rapid proliferation of malware poses a significant challenge regarding digital security, necessitating the development of advanced techniques for malware detection and categorization. In this study, we investigate Android malware detection and categorization using a two-step machine learning (ML) framework combined with feature engineering. The proposed framework first performs binary categorization to detect malware and then applies multi-class categorization to categorize malware into types, such as adware, banking Trojans, SMS malware, and riskware. Feature selection techniques such as chi-squared testing and select-from-model (SFM) were employed to reduce dimensionality and enhance model performance. Various ML classifiers were evaluated, and the proposed model achieved outstanding accuracy, at 97.82% for malware detection and 96.09% for malware categorization. The proposed framework outperforms existing approaches, demonstrating the effectiveness of feature engineering and random forest (RF) models in addressing computational efficiency. This research contributes a robust and interpretable framework for Android malware detection that is resource-efficient and practical for use in real-world applications. It also offers a scalable approach via which practitioners can deploy efficient malware detection systems. Future work will focus on real-time implementation and adaptive methodologies to address evolving malware threats.