EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning

TL;DR

This study compares Android malware detection effectiveness between emulator-based and real device-based dynamic analysis using machine learning, showing better feature extraction and higher analysis success on real devices.

Contribution

The paper introduces a tool for on-device dynamic feature extraction and provides a comparative analysis demonstrating improved malware detection on real devices over emulators.

Findings

More features are effectively extracted from real devices.

Approximately 24% more apps are successfully analyzed on phones.

Machine learning detection performs better with on-device features.

Abstract

The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious activities from analysis tools. These include a wide range of anti-emulator techniques, where the malware programs attempt to hide their malicious activities by detecting the emulator. For this reason, countermeasures against antiemulation are becoming increasingly important in Android malware detection. Analysis and detection based on real devices can alleviate the problems of anti-emulation as well as improve the effectiveness of dynamic analysis. Hence, in this paper we present an investigation of machine learning based malware detection using dynamic analysis on real devices. A tool is implemented to automatically extract dynamic features from Android phones and through several experiments, a comparative analysis of emulator based vs. device based detection by means of several machine learning algorithms is undertaken. Our study shows that several features could be extracted more effectively from the on-device dynamic analysis compared to emulators. It was also found that approximately 24% more apps were successfully analysed on the phone. Furthermore, all of the studied machine learning based detection performed better when applied to features extracted from the on-device dynamic analysis.