Android malware continues to pose significant security threats, with evolving tactics that often bypass traditional detection systems. Existing detection mechanisms remain ineffective against obfuscated or novel malware variants, necessitating the development of more robust detection techniques. This study introduces a comprehensive machine learning framework for Android malware detection that leverages a systematic comparison between a deep Neural Network and diverse ensemble methods, including Voting Ensemble, Stacking Ensemble, XGBoost, and Random Forest. Unlike prior studies that often focus on individual approaches, this work provides an empirical benchmark that demonstrates how practical ensemble configurations can achieve superior performance while maintaining computational efficiency. The model is trained using the CIC-AndMal2017 dataset, incorporating a comprehensive set of static features, including API calls, permissions, services, receivers, and activities. Feature selection was performed to optimize model performance, reducing redundancy and improving detection accuracy. The models were evaluated on multiple classification metrics, including accuracy, F1-score, and confusion matrices, with the Voting Ensemble model achieving an accuracy of 94.14%, outperforming all other approaches, including the deep neural network. This study contributes to the field by demonstrating that a carefully constructed ensemble of diverse classifiers can not only improve detection accuracy but also offer a more scalable, lightweight solution compared to complex deep learning models. The research provides a significant advancement in practical Android malware detection by identifying optimal strategies that balance performance with computational efficiency.
Museeb et al. (Wed,) studied this question.