Automated Malware Detection in Mobile App Stores Based on Robust Feature Generation

Abstract

Many Internet of Things (IoT) services are currently tracked and regulated via mobile devices, making them vulnerable to privacy attacks and exploitation by various malicious applications. Current solutions are unable to keep pace with the rapid growth of malware and are limited by low detection accuracy, long discovery time, complex implementation, and high computational costs associated with the processor speed, power, and memory. Therefore, an automated intelligence technique is necessary for detecting apps containing malware and effectively predicting cyberattacks in mobile marketplaces. In this study, a system for classifying mobile marketplaces applications using real-world datasets is proposed, which analyzes the source code to identify malicious apps. A rich feature set of application programming interface (API) calls is proposed to capture the regularities in apps containing malicious content. Two feature-selection methods—Chi-Square and ANOVA—were examined in conjunction with ten supervised machine-learning algorithms. The detection accuracy of each classifier was evaluated to identify the most reliable classifier for malware detection using various feature sets. Chi-Square was found to have a higher detection accuracy as compared to ANOVA. The proposed system achieved a detection accuracy of 98.1% with a classification time of 1.22 s. Furthermore, the proposed system required a reduced number of API calls (500 instead of 9000) to be incorporated as features.