Proteus: Detecting Android Emulators from Instruction-Level Profiles

Abstract

The popularity of Android and the personal information stored on these devices attract the attention of regular cyber-criminals as well as nation state adversaries who develop malware that targets this platform. To identify malicious Android apps at a scale (e.g., Google Play contains 3.7M Apps), state-of-the-art mobile malware analysis systems inspect the execution of apps in emulation-based sandboxes. An emerging class of evasive Android malware, however, can evade detection by such analysis systems through ceasing malicious activities if an emulation sandbox is detected. Thus, systematically uncovering potential methods to detect emulated environments is crucial to stay ahead of adversaries. This work uncovers the detection methods based on discrepancies in instruction-level behavior between software-based emulators and real ARM CPUs that power the vast majority of Android devices. To systematically discover such discrepancies at scale, we propose the Proteus system. Proteus performs large-scale collection of application execution traces (i.e., registers and memory) as they run on an emulator and on accurate software models of ARM CPUs. Proteus automatically identifies the instructions that cause divergent behavior between emulated and real CPUs and, on a set of 500K test programs, identified 28K divergent instances. By inspecting these instances, we reveal 3 major classes of root causes that are responsible for these discrepancies. We show that some of these root causes can be easily fixed without introducing observable performance degradation in the emulator. Thus, we have submitted patches to improve resilience of Android emulators against evasive malware.