A Stacking Ensemble Framework for Android Malware Prediction

Every Android application needs the collection of permissions during installation time, and these can be used in permission-based malware detection. Different ensemble strategies for categorising Android malware have recently received much more attention than traditional methodologies. In this paper, classification performance of one of the primary ensemble approach (Stacking) in R libraries in context of for Android malware is proposed. The presented technique reserves both the desirable qualities of an ensemble technique, diversity, and accuracy. The proposed technique produced significantly better results in terms of categorisation accuracy.