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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.
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WatchID: Wearable Device Authentication via Reprogrammable Vibration
Prevalent wearables (e.g., smartwatches and activity trackers) demand high secure measures to protect users' private information, such as personal contacts, bank accounts, etc. While existing two-factor authentication methods can enhance traditional user authentication, they are not convenient as they require participations from users. Recently, manufacturing imperfections in hardware devices (e.g., accelerometers and WiFi interface) have been utilized for low-effort two-factor authentications. However, these methods rely on fixed device credentials that would require users to replace their devices once the device credentials are stolen. In this work, we develop a novel device authentication system, WatchID, that can identify a user's wearable using its vibration-based device credentials. Our system exploits readily available vibration motors and accelerometers in wearables to establish a vibration communication channel to capture wearables' unique vibration characteristics. Compared to existing methods, our vibration-based device credentials are reprogrammable and easy to use. We develop a series of data processing methods to mitigate the impact of noises and body movements. A lightweight convolutional neural network is developed for feature extraction and device authentication. Extensive experimental results using five smartwatches show that WatchID can achieve an average precision and recall of 98% and 94% respectively in various attacking scenarios.
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- Award ID(s):
- 2028873
- PAR ID:
- 10393749
- Editor(s):
- Hara, T.; Yamaguchi, H.
- Date Published:
- Journal Name:
- Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
- Volume:
- 419
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
