Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing
Abstract
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.
Fields of Study & Subjects
Advanced Malware Detection Techniques
IoT and GPS-based Vehicle Safety Systems
Software Engineering Research
Publication details
- Journal / source
- Journal of Computing Theories and Applications
- Publisher
- IntSys Research
- Volume
- 3
- Issue
- 3
- Pages
- 302–313
- Publication date
- 18 January 2026