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Don't Skype & Type!: Acoustic Eavesdropping in Voice-Over-IP

Published:02 April 2017Publication History

ABSTRACT

Acoustic emanations of computer keyboards represent a serious privacy issue. As demonstrated in prior work, physical properties of keystroke sounds might reveal what a user is typing. However, previous attacks assumed relatively strong adversary models that are not very practical in many real-world settings. Such strong models assume: (i) adversary's physical proximity to the victim, (ii) precise profiling of the victim's typing style and keyboard, and/or (iii) significant amount of victim's typed information (and its corresponding sounds) available to the adversary.

This paper presents and explores a new keyboard acoustic eavesdropping attack that involves Voice-over-IP (VoIP), called Skype & Type (S&T), while avoiding prior strong adversary assumptions. This work is motivated by the simple observation that people often engage in secondary activities (including typing) while participating in VoIP calls. As expected, VoIP software acquires and faithfully transmits all sounds, including emanations of pressed keystrokes, which can include passwords and other sensitive information. We show that one very popular VoIP software (Skype) conveys enough audio information to reconstruct the victim's input -- keystrokes typed on the remote keyboard. Our results demonstrate that, given some knowledge on the victim's typing style and keyboard model, the attacker attains top-5 accuracy of 91.7% in guessing a random key pressed by the victim.

Furthermore, we demonstrate that S&T is robust to various VoIP issues (e.g., Internet bandwidth fluctuations and presence of voice over keystrokes), thus confirming feasibility of this attack. Finally, it applies to other popular VoIP software, such as Google Hangouts.

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          cover image ACM Conferences
          ASIA CCS '17: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security
          April 2017
          952 pages
          ISBN:9781450349444
          DOI:10.1145/3052973

          Copyright © 2017 ACM

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          Publication History

          • Published: 2 April 2017

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          ASIA CCS '17 Paper Acceptance Rate67of359submissions,19%Overall Acceptance Rate418of2,322submissions,18%

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