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The popularity of smart home devices has led to an increase in security incidents happening in smart homes. A key measure to avoid such incidents is to authenticate users before they can interact with smart devices. However, current methods often require additional hardware. This article proposes STATION, a gesture-based authentication system, an effective gesture-based authentication method built on top of the voice interfaces already available in these smart home devices, without adding new hardware. STATION uses a gesture processing pipeline that identifies Doppler-existing frames and detects the direction of arrival of Reflection to authenticate users in low SNR environments and at longer distances. Furthermore, regarding the nature of gesture-based authentication, this system also supports detecting user liveness, preventing replay and synthesis attacks from remote attackers. The evaluation of STATION shows high accuracy with a false acceptance rate (FAR) of 0.08% and false rejection rate (FRR) of 3.10% for users within 1.5 m of the device. 

It has been demonstrated in numerous previous studies that Android and its underlying Linux operating systems do not properly isolate mobile apps to prevent cross-app side- channel attacks. Cross-app information leakage enables malicious Android apps to infer sensitive user data (e.g., passwords), or private user information (e.g., identity or location) without requiring specific permissions. Nevertheless, no prior work has ever studied these side-channel attacks on iOS-based mobile devices. One reason is that iOS does not implement procfs— the most popular side-channel attack vector; hence the previously known attacks are not feasible. In this paper, we present the first study of OS-level side-channel attacks on iOS. Specifically, we identified several new side-channel attack vectors (i.e., iOS APIs that enable cross-app information leakage); developed machine learning frameworks (i.e., classification and pattern matching) that combine multiple attack vectors to improve the accuracy of the inference attacks; demonstrated three categories of attacks that exploit these vectors and frameworks to exfiltrate sensitive user information. We have reported our findings to Apple and proposed mitigations to the attacks. Apple has incorporated some of our suggested countermeasures into iOS 11 and MacOS High Sierra 10.13 and later versions.