Android Malware Classification with Feature Selection using Artificial Bee Colony Algorithm

Abstract

The proliferation of Android devices has resulted in a rise in complex malware specifically designed for these platforms, requiring higher detection techniques beyond conventional static and dynamic analyses. In this study, the Artificial Bee Colony (ABC) algorithm for feature selection is integrated with the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) classifiers to provide a novel method for Android malware detection. The ABC algorithm, which draws inspiration from honeybee foraging behavior, improves the performance of classifiers by balancing exploration and exploitation within feature subsets. Evaluation of the suggested approach on the Debrin Android malware dataset showed significant enhancements in detection accuracy and decreased false positives. The experimental findings demonstrated that both RF and XGBoost classifiers showed excellent performance, with RF slightly surpassing XGBoost in accuracy, precision, recall, and ROC-AUC metrics. The results highlight the efficacy of integrating metaheuristic feature selection with strong classifiers to enhance Android malware detection and tackle the difficulties presented by progressing threats.