Binary grasshopper optimization based feature selection for mobile malware detection using random forest

The most widely used and dominant operating system for smartphones is Android. Over a million Android mobile applications are available in the Google Play store, which users download and use for various purposes. In the meantime, they have become a prominent target for immoral incursions as a result of their open-source platform and popularity. Due to their inability to recognize and predict malware with varying features, conventional techniques in this investigation failed to detect sophisticated malware. In recent years, machine learning classification methods have been used to tackle these problems, and this study discovers that ensemble learning yields the best outcomes. Consequently, this study suggested an Ensemble learning classification model trained on the (Malgenome, CIC-MalDroid2020, and Drebin) mobile malware dataset using Random Forest with Binary Grasshopper Optimization Algorithm. The model’s performance is assessed. We achieved a maximum accuracy of 97% for the Drebin dataset, the highest accuracy of 98.70% for the Malgenom dataset, and the highest accuracy of 97.70% for the CIC-MalDroid2020 dataset.