GHR-Optimizer: An ensemble-based feature selection approach for classifying android malware

Abstract

This study delves into advanced feature selection methodologies for enhancing Android malware classification. GHR-Optimizer is introduced as an innovative feature selection approach combining Grey Wolf Optimization, Hill Climbing, and Random Forest Classifier method. The approach selects features from a hybrid dataset and is evaluated across machine learning, deep learning, and ensemble frameworks. A detailed comparative analysis is conducted, contrasting GHR-Optimizer with static and dynamic feature sets as well as traditional filter and wrapper-based methods. The implementation of the GHR method demonstrated superior performance, particularly when evaluated with diverse datasets such as KronoDroid, which achieved exceptional accuracy and balance in classification metrics. When integrated with the Random Forest classifier, the GHR-Optimizer achieves an accuracy of 98.40%. These findings underscore GHR-Optimizer’s superior performance in boosting classification accuracy and robustness, highlighting its pivotal role in advancing feature selection strategies within the domain.