Detecting Malware in Android Applications using XGBoost

Abstract

The omnipresence of Android devices and the amount of sensitive information kept in them makes detecting malware in Android applications crucial. In this paper, the efficacy of using machine learning models for the purpose of malware detection in Android applications was examined, and several XGBoost models were developed and compared - each with a distinct feature set. We used the f1 score, precision, recall, confusion matrices, and precision-recall curves to compare the models. Accuracy was not considered since we needed a balanced dataset. One of the models we developed, which used all the available features in the dataset, had encouraging results with high precision and recall.