An efficient Android malware detection method using Borutashap algorithm

Abstract

The Android operating system captures the largest global smartphone market share. However, its popularity and open-source nature have garnered the attention of cybercriminals. The landscape of Android malware has evolved significantly over time. Traditional techniques for detecting Android malware are encountering difficulties in keeping up with this evolution. Specifically, methods that rely on extracting various features from Android applications are becoming difficult to implement as high-dimensional feature sets incur huge computational overheads when employed with machine learning algorithms. Therefore, this research proposes using Bortua and BorutaShap feature selection algorithms to choose features that contribute to detecting malicious Android applications. It uses static and dynamic features of Android applications to create a detection model for verification and evaluation of the mentioned algorithms. Experimental results showed that Bortua and BorutaShap algorithms offer promising results by achieving the highest accuracy of approximately 99%.