research-article

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

Authors:

Alberto Compagno,

Gene TsudikAuthors Info & Claims

ASIA CCS '17: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security

Pages 703 - 715

https://doi.org/10.1145/3052973.3053005

Published: 02 April 2017 Publication 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.

References

[1]

2015: Skype's year in review. url: http://blogs.skype.com/2015/12/17/2015-skypes-year-in-review/ (visited on 06/29/2016).

[2]

Kamran Ali et al. "Keystroke recognition using WiFi signals". In: ACM MobiCom. 2015, pp. 90--102.

Digital Library

[3]

Dmitri Asonov and Rakesh Agrawal. "Keyboard acoustic emanations". In: IEEE S&P. 2004, pp. 3--11.

[4]

Davide Balzarotti, Marco Cova, and Giovanni Vigna. "Clearshot: Eavesdropping on keyboard input from video". In: IEEE S&P. 2008, pp. 170--183.

Digital Library

[5]

Yigael Berger, Avishai Wool, and Arie Yeredor. "Dictionary attacks using keyboard acoustic emanations". In: ACM CCS. 2006, pp. 245--254.

Digital Library

[6]

Stephen Boyd et al. "Accuracy at the top". In: NIPS. 2012, pp. 953--961.

Digital Library

[7]

Stuart Card, Thomas Moran, and Allen Newell. "The keystroke-level model for user performance time with interactive systems". In: CACM 7 (1980), pp. 396--410.

Digital Library

[8]

Anupam Das, Nikita Borisov, and Matthew Caesar. "Do you hear what I hear?: fingerprinting smart devices through embedded acoustic components". In: ACM CCS. 2014, pp. 441--452.

Digital Library

[9]

Jeffrey Friedman. "Tempest: A signal problem". In: NSA Cryptologic Spectrum (1972).

[10]

Isabelle Guyon et al. "Gene selection for cancer classification using support vector machines". In: Machine Learning 1-3 (2002), pp. 389--422.

Digital Library

[11]

Tzipora Halevi and Nitesh Saxena. "A closer look at keyboard acoustic emanations: random passwords, typing styles and decoding techniques". In: ACM CCS. 2012, pp. 89--90.

Digital Library

[12]

Tzipora Halevi and Nitesh Saxena. "Keyboard acoustic side channel attacks: exploring realistic and security-sensitive scenarios". In: International Journal of Information Security 5 (2015), pp. 443--456.

Digital Library

[13]

Tadayoshi Kohno, Andre Broido, and Kimberly Claffy. "Remote physical device fingerprinting". In: IEEE TDSC 2 (2005), pp. 93--108.

Digital Library

[14]

Paul Lamere et al. "The CMU SPHINX-4 speech recognition system". In: IEEE ICASSP. 2003, pp. 2--5.

[15]

Jian Liu et al. "Snooping keystrokes with mm-level audio ranging on a single phone". In: ACM MobiCom. 2015, pp. 142--154.

Digital Library

[16]

Beth Logan et al. "Mel Frequency Cepstral Coefficients for Music Modeling." In: ISMIR. 2000.

[17]

Jan Lukas, Jessica Fridrich, and Miroslav Goljan. "Digital camera identification from sensor pattern noise". In: IEEE TIFS 2 (2006), pp. 205--214.

Digital Library

[18]

Philip Marquardt et al. "(sp) iPhone: decoding vibrations from nearby keyboards using mobile phone accelerometers". In: ACM CCS. 2011, pp. 551--562.

Digital Library

[19]

Zdenek Martinasek, Vlastimil Clupek, and Krisztina Trasy. "Acoustic attack on keyboard using spectrogram and neural network". In: TSP. 2015, pp. 637--641.

[20]

Microsoft BUILD 2016 Keynote. url: https://channel9.msdn.com/Events/Build/2016/KEY01 (visited on 06/29/2016).

[21]

Opus Codec Support. url: https://wiki.xiph.org/OpusSupport (visited on 07/19/2016).

[22]

Over 1 billion Skype mobile downloads. url: http://blogs.skype.com/2016/04/28/over-1-billion-skype-mobile-downloads-thank-you/ (visited on 06/29/2016).

[23]

Oxford Dictionary - Which letters in the alphabet are used most often. url: http://www.oxforddictionaries.com/words/which-letters-are-used-most (visited on 06/29/2016).

[24]

EH Rothauser et al. "IEEE recommended practice for speech quality measurements". In: IEEE Transactions on Audio and Electroacoustics 3 (1969), pp. 225--246.

[25]

Diksha Shukla et al. "Beware, your hands reveal your secrets!" In: ACM CCS. 2014, pp. 904--917.

Digital Library

[26]

Jean-Marc Valin, Koen Vos, and T Terriberry. "Definition of the Opus audio codec". In: IETF, September (2012).

[27]

Martin Vuagnoux and Sylvain Pasini. "Compromising Electromagnetic Emanations of Wired and Wireless Keyboards." In: USENIX Security. 2009, pp. 1--16.

Digital Library

[28]

Junjue Wang et al. "Ubiquitous keyboard for small mobile devices: harnessing multipath fading for fine-grained keystroke localization". In: ACM MobiSys. 2014, pp. 14--27.

Digital Library

[29]

RL Wegel and CE Lane. "The auditory masking of one pure tone by another and its probable relation to the dynamics of the inner ear". In: Physical Review 2 (1924), p. 266.

[30]

Teng Wei et al. "Acoustic eavesdropping through wireless vibrometry". In: ACM MobiCom. 2015, pp. 130--141.

Digital Library

[31]

Tong Zhu et al. "Context-free attacks using keyboard acoustic emanations". In: ACM CCS. 2014, pp. 453--464.

Digital Library

[32]

Li Zhuang, Feng Zhou, and Doug Tygar. "Keyboard acoustic emanations revisited". In: ACM TISSEC 1 (2009), p. 3.

Digital Library

Cited By

Spata MMaria Russo VOrtis ABattiato S(2025)A New Pipeline for Snooping Keystroke Based on Deep Learning AlgorithmIEEE Access10.1109/ACCESS.2025.353687713(24498-24514)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3536877
Spata MRusso VOrtis ABattiato S(2024)Acoustic Side Channel Attack for Keystroke Splitting in the Wild2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)10.1109/MetroXRAINE62247.2024.10796234(131-136)Online publication date: 21-Oct-2024
https://doi.org/10.1109/MetroXRAINE62247.2024.10796234
Chen YLi WCheng XHu P(2024)A survey of acoustic eavesdropping attacks: Principle, methods, and progressHigh-Confidence Computing10.1016/j.hcc.2024.1002414:4(100241)Online publication date: Dec-2024
https://doi.org/10.1016/j.hcc.2024.100241
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Index Terms

Don't Skype & Type!: Acoustic Eavesdropping in Voice-Over-IP
1. Security and privacy

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Published In

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

General Chairs:
Ramesh Karri
New York University, New York, USA
,
Ozgur Sinanoglu
New York University Abu Dhabi, UAE
,
Program Chairs:
Ahmad-Reza Sadeghi
Technische Universität Darmstadt, Germany
,
Xun Yi
RMIT University, Australia

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 April 2017

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ASIA CCS '17

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SIGSAC

ASIA CCS '17: ACM Asia Conference on Computer and Communications Security

April 2 - 6, 2017

Abu Dhabi, United Arab Emirates

Acceptance Rates

ASIA CCS '17 Paper Acceptance Rate 67 of 359 submissions, 19%;

Overall Acceptance Rate 418 of 2,322 submissions, 18%

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Cited By

Spata MMaria Russo VOrtis ABattiato S(2025)A New Pipeline for Snooping Keystroke Based on Deep Learning AlgorithmIEEE Access10.1109/ACCESS.2025.353687713(24498-24514)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3536877
Spata MRusso VOrtis ABattiato S(2024)Acoustic Side Channel Attack for Keystroke Splitting in the Wild2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)10.1109/MetroXRAINE62247.2024.10796234(131-136)Online publication date: 21-Oct-2024
https://doi.org/10.1109/MetroXRAINE62247.2024.10796234
Chen YLi WCheng XHu P(2024)A survey of acoustic eavesdropping attacks: Principle, methods, and progressHigh-Confidence Computing10.1016/j.hcc.2024.1002414:4(100241)Online publication date: Dec-2024
https://doi.org/10.1016/j.hcc.2024.100241
Taheritajar AHarris ZRahaeimehr R(2024)A Survey on Acoustic Side Channel Attacks on KeyboardsInformation and Communications Security10.1007/978-981-97-8798-2_6(99-121)Online publication date: 25-Dec-2024
https://doi.org/10.1007/978-981-97-8798-2_6
Ozkan AKilic BAcarturk C(2024)Keystroke Transcription from Acoustic Emanations Using Continuous Wavelet TransformMachine Learning for Cyber Security10.1007/978-981-97-2458-1_1(1-16)Online publication date: 23-Apr-2024
https://doi.org/10.1007/978-981-97-2458-1_1
Spata MOrtis ABattiato SRusso V(2024)A New Deep Learning Pipeline for Acoustic Attack on KeyboardsIntelligent Systems and Applications10.1007/978-3-031-66329-1_26(402-414)Online publication date: 31-Jul-2024
https://doi.org/10.1007/978-3-031-66329-1_26
Tu YShan LHossen MRampazzi SButler KHei XCalandrino JTroncoso C(2023)Auditory eyesightProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620248(175-192)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620248
Long YYan CXiao SPrasad SXu WFu K(2023)Private Eye: On the Limits of Textual Screen Peeking via Eyeglass Reflections in Video Conferencing2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179423(3432-3449)Online publication date: May-2023
https://doi.org/10.1109/SP46215.2023.10179423
Amodei ACapriglione DFerrigno LMiele GTari LTomasso GCerro G(2023)Experimental Analysis of Side-Channel Emissions for IoT Devices Activities’ Profiling2023 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)10.1109/MetroInd4.0IoT57462.2023.10180188(42-47)Online publication date: 6-Jun-2023
https://doi.org/10.1109/MetroInd4.0IoT57462.2023.10180188
Harrison JToreini EMehrnezhad M(2023)A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW59978.2023.00034(270-280)Online publication date: Jul-2023
https://doi.org/10.1109/EuroSPW59978.2023.00034
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