Name
Abstract | ||
|---|---|---|
Voice has become an increasingly popular User Interaction (UI) channel, mainly contributing to the current trend of wearables, smart vehicles, and home automation systems. Voice assistants such as Alexa, Siri, and Google Now, have become our everyday fixtures, especially when/where touch interfaces are inconvenient or even dangerous to use, such as driving or exercising. The open nature of the voice channel makes voice assistants difficult to secure, and hence exposed to various threats as demonstrated by security researchers. To defend against these threats, we present VAuth, the first system that provides continuous authentication for voice assistants. VAuth is designed to fit in widely-adopted wearable devices, such as eyeglasses, earphones/buds and necklaces, where it collects the body-surface vibrations of the user and matches it with the speech signal received by the voice assistant's microphone. VAuth guarantees the voice assistant to execute only the commands that originate from the voice of the owner. We have evaluated VAuth with 18 users and 30 voice commands and find it to achieve 97% detection accuracy and less than 0.1% false positive rate, regardless of VAuth's position on the body and the user's language, accent or mobility. VAuth successfully thwarts various practical attacks, such as replay attacks, mangled voice attacks, or impersonation attacks. It also incurs low energy and latency overheads and is compatible with most voice assistants.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3117811.3117823 | MobiCom '17: The 23rd Annual International Conference on Mobile Computing and Networking
Snowbird
Utah
USA
October, 2017 |
DocType | Volume | ISBN |
Conference | abs/1701.04507 | 978-1-4503-4916-1 |
Citations | PageRank | References |
22 | 0.81 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| huan feng | 1 | 45 | 4.43 |
| Kassem Fawaz | 2 | 190 | 19.60 |
| Kang G. Shin | 3 | 14055 | 1487.46 |