Machine learning-based malware detection on Android devices using behavioral features

The most recent focus of security vulnerabilities has been on Android devices. Several permissions were developed to detect malware, but they can affect the device through malware applications that deal with dangerous permissions. To detect malware, behavioral features and machine learning techniques are used in this work. The purpose of this work is to evaluate the impact of malware occurrence using a behavioral features dataset. For malware detection, the features are incorporated into the prediction model using LBGM, ENB0, and EEXR. There is a Collector Phase, a Cleaner Phase, a Selector Phase, and a Detector Phase in this proposed system. This proposed system does the relevant process for detecting malware through these four phases. According to the performance results, the proposed prediction model EEXR gives high accuracy of 96.75% and less error rate of 3.28% while predicting more than other methods. Copyright (C) 2022 Elsevier Ltd. All rights reserved.