Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing

This study presents a comparative analysis of machine learning algorithms for Android malware detection using the TUANDROMD dataset. SMOTE was applied to address class imbalance and ensure robust model evaluation. Experimental results show that Random Forest achieved the best performance with near-perfect accuracy and ROC AUC, confirming its robustness for malware detection tasks.