연구 분야: Analysis
학회: ACM Transactions on Internet of Things, Volume 4, Issue 4
Keystroke snooping is an effective way to steal sensitive information from the victims. Recent research on acoustic emanation-based techniques has greatly improved the accessibility by non-professional adversaries. However, these approaches either require multiple smartphones or require specific placement of the smartphone relative to the keyboards, which tremendously restricts the application scenarios. In this article, we propose UltraSnoop, a training-free, transferable, and placement-agnostic scheme that manages to infer user’s input using a single smartphone placed within the range covered by a microphone and speaker. The innovation of Ultrasnoop is that we propose an ultrasonic anchor-keystroke positioning method and a Mel Frequency Cepstrum Coefficients clustering algorithm, synthesis of which could infer the relative position between the smartphone and the keyboard. Along with the keystroke time difference of arrival, our method could infer the keystrokes and even gradually improve the accuracy as the snooping proceeds. Our real-world experiments show that UltraSnoop could achieve more than 85% top-3 snooping accuracy when the smartphone is placed within the range of 30–60 cm from the keyboard.
| 발행 연도 | 2023년 |
|---|---|
| 인용수 | 3 |
| 출판 국가 | Andorra, China |
| 사이트 | ACM |
| 좋아요 수 | 0 |