Detecting Android Side Channel Probing Attacks Based on System States

Abstract

Side channels are actively exploited by attackers to infer users’ privacy from publicly-available information on Android devices, where attackers probe the states of system components (e.g., CPU and memory), APIs, and device sensors (e.g., gyroscope and microphone). These information can be accessed by applications without any additional permission. As a result, traditional permission-based solutions cannot efficiently prevent/detect these probing attacks. In this paper, we systematically analyze the Android side-channel probing attacks, and observe that the high frequency sensitive data collecting operations from a malicious app caused continuous changes of its process states. Based on this observation, we propose SideGuard, a process-state-based approach to detect side-channel probing attacks. It monitors the process states of the applications and creates the corresponding behavior models described by feature vectors. Based on the application behavior models, we train and obtain classifiers to detect malicious app behaviors by using learning-based classification techniques. We prototyped and evaluated our approach. The experiment results demonstrate the effectiveness of our approach.