Analysis of Permission Selection Techniques in Machine Learning-based Malicious App Detection

Abstract

With the increasing popularity of the Android platform, we have seen the rapid growth of malicious Android applications recently. Considering that the heavy use of applications on mobile phones such as games, emails, and social network services has become a crucial part of our daily life, we have become more vulnerable to malicious applications running on mobile devices. To alleviate this hostile environment of Android mobile applications, we propose a malware detection approach that (1) extracts both built-in permissions and custom permissions requested by Android apps from their Manifest.xml and (2) applies the permissions and a Random Forest classifier to Android applications for classifying them into benign and malicious. The Random Forest classifier learns a model using the permissions to classify the input dataset of 45,311 Android applications. In the learned model, an optimal subset of permissions has been identified and then using the subset of permissions we could achieve 94.23% accuracy in detecting malware.