Volume 18, No. 6, 2021

Multilevel Ensemble Model For Prediction Of Android Malware

Ravneet Singh Bedi and Prashant Singh Rana


Early identification of malicious applications can help the Android user register private data and device integrity. It is imperative to introduce a reliable system with high precision and effectiveness for predicting. In this study, features extracted from intermediatecode representations obtained using de compilation of APK file are used for providing requi- site input data to develop the models for predicting android malware applications. A new multi-tier set model is developed for the prediction of Android Malware. Under this overar- ching approach, seven different machine learning models are combined to predict Android malware. The proposed model reaches 97.56

Pages: 4316-4327

Keywords: Machine learning models; Multilevel ensemble model; Regularized trees; An- droid Malware;

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