An optimized Stacking Ensemble Framework for Malware Detection Using XGBoost and Random Forest (Journal of Academic Research, 2026)
. Android devices are increasingly targeted by malware due to their widespread adoption and open ecosystem. Traditional signature-based detection methods are insufficient for evolving threats, highlighting the need for intelligent approaches. This study proposes an enhanced Android malware classification framework using XGBoost, Random Forest for feature selection, and a stacking ensemble. The CICMalDroid 2020 dataset was preprocessed through outlier removal, robust scaling, binary transformation, and SMOTE-based class balancing. Experimental results show that the stacking ensemble achieves the highest detection performance with 98.14% accuracy, 93.62% F1-score, and 99.63% ROC-AUC, outperforming individual models. Additionally, using RF as features selector  improves computational efficiency, reducing training and testing times. These findings demonstrate that integrating advanced machine learning techniques enhances both the effectiveness and efficiency of Android malware detection.
Keywords: XGBoost; Feature Selection; Stacking Ensemble; CICMalDroid 2020; SMOTE