Soft Computing

, Volume 23, Issue 21, pp 11063–11075 | Cite as

LOL: localization-free online keystroke tracking using acoustic signals

  • Zhenquan Qin
  • Jiaxin Du
  • Guangjie HanEmail author
  • Gaopeng Yong
  • Linlin Guo
  • Lei Wang
Methodologies and Application


Contents typed via keyboards prove to be vulnerable to attacks based on acoustic emanations analysis. However, previous works achieve the attacks under controlled environment, e.g., neglecting the noises or requiring the keyboard to be located in fixed locations. In this study, we present a localization-free online keystroke tracking system (LOL), which enables people to use prior knowledge obtained from the keyboard in one location to recognize real-time keystrokes of the same type of keyboard in any other places, despite various background noises. Combined with support vector machine, we design an detection model to separate keystroke signals from noises. By analyzing the properties of acoustics transmission, we propose an angle-based sampling method with a single microphone to decrease the dependence on certain locations, and it also increases the diversity of signals in the meantime. Our real-world experiments demonstrate a 99.47% keystroke detection rate, a 97.27% recognition accuracy under ideal condition, and an 84.55% content recovery accuracy despite changing locations of the keyboard. Most commercial off-the-shelf sound recording devices, e.g., smartphones, can be used in our system to record acoustic emanations from keystrokes. LOL could attract more community to study security of keyboard devices and promote users to enhance privacy protection awareness.


Keystroke tracking Acoustic signals Localization-free Angle-based sampling 



The work is supported by the Fundamental Research Funds for the Central Universities, No. DUT17RC(3)094, the Fundamental Research Funds for the Central University with No. DUT17 LAB16 and the Program for Liaoning Excellent Talents in University, No. LR2017009.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Ali K, Liu AX, Wang W, Shahzad M (2015) Keystroke recognition using wifi signals. In: ACM MobiComGoogle Scholar
  2. Ali K, Liu AX, Wang W, Shahzad M (2017) Recognizing keystrokes using WiFi devices. IEEE J Sel Areas Commun PP(99): 1–1Google Scholar
  3. Asonov D, Agrawal R (2004) Keyboard acoustic emanations. In: IEEE symposium on security and privacyGoogle Scholar
  4. Baynath P, Soyjaudah KMS, Khan HM (2017) Keystroke recognition using neural network. In: International symposium on computational and business intelligence, pp 86–90Google Scholar
  5. Berger Y, Wool A, Yeredor A (2006) Dictionary attacks using keyboard acoustic emanations. In: ACM CCSGoogle Scholar
  6. Chen B, Yenamandra V, Srinivasan K (2015) Tracking keystrokes using wireless signals. In: ACM MobiSysGoogle Scholar
  7. Chen H, Li F, Wang Y (2017) Echotrack: acoustic device-free hand tracking on smart phones. In: IEEE INFOCOMGoogle Scholar
  8. Li F, Wang X, Chen H, Sharif K, Wang Y (2017) Clickleak: keystroke leaks through multimodal sensors in cyber-physical social networks. IEEE Access 5:27311–27321CrossRefGoogle Scholar
  9. Li M, Meng Y, Liu J, Zhu H, Liang X, Liu Y, Ruan N (2016) When CSI meets public WiFi: inferring your mobile phone password via WiFi signals. In: ACM CCSGoogle Scholar
  10. Liu J, Wang Y, Kar G, Chen Y, Yang J, Gruteser M (2015) Snooping keystrokes with mm-level audio ranging on a single phone. In: ACM MobiComGoogle Scholar
  11. Liu X, Zhou Z, Diao W, Li Z, Zhang K (2015) When good becomes evil: keystroke inference with smartwatch. In: ACM CCSGoogle Scholar
  12. Maiti A, Armbruster O, Jadliwala M, He J (2016) Smartwatch-based keystroke inference attacks and context-aware protection mechanisms. In: ACM CCSGoogle Scholar
  13. Mao W, He J, Qiu L (2016) CAT: high-precision acoustic motion tracking. In: ACM MobiComGoogle Scholar
  14. Marquardt P, Verma A, Carter H, Traynor P (2011) (sp) iphone: decoding vibrations from nearby keyboards using mobile phone accelerometers. In: ACM CCSGoogle Scholar
  15. Miluzzo E, Varshavsky A, Balakrishnan S, Choudhury RR (2012) Tapprints: your finger taps have fingerprints. In: ACM MobiSysGoogle Scholar
  16. Nirjon S, Gummeson J, Gelb D, Kim KH (2015) Typingring: a wearable ring platform for text input. In: ACM MobiSysGoogle Scholar
  17. Raguram R, White AM, Goswami D, Monrose F, Frahm JM (2011) ispy: automatic reconstruction of typed input from compromising reflections. In: ACM CCSGoogle Scholar
  18. Shukla D, Kumar R, Serwadda A, Phoha VV (2014) Beware, your hands reveal your secrets! In: ACM CCSGoogle Scholar
  19. Wang H, Lai TTT, Roy Choudhury R (2015) Mole: motion leaks through smartwatch sensors. In: ACM MobiComGoogle Scholar
  20. Wang J, Ruby R, Wang L, Wu K (2016) Accurate combined keystrokes detection using acoustic signals. In: IEEE MSNGoogle Scholar
  21. Wang J, Zhao K, Zhang X, Peng C (2014) Ubiquitous keyboard for small mobile devices: harnessing multipath fading for fine-grained keystroke localization. In: ACM MobiSysGoogle Scholar
  22. Wang W, Liu AX, Sun K (2016) Device-free gesture tracking using acoustic signals. In: ACM MobiComGoogle Scholar
  23. Xu Y, Heinly J, White AM, Monrose F, Frahm JM (2013) Seeing double: reconstructing obscured typed input from repeated compromising reflections. In: ACM CCSGoogle Scholar
  24. Yin Y, Li Q, Xie L, Yi S, Novak E, Lu S (2016) Camk: a camera-based keyboard for small mobile devices. In: IEEE INFOCOMGoogle Scholar
  25. Yue Q, Ling Z, Fu X, Liu B, Ren K, Zhao W (2014) Blind recognition of touched keys on mobile devices. In: ACM CCSGoogle Scholar
  26. Zhu T, Ma Q, Zhang S, Liu Y (2014) Context-free attacks using keyboard acoustic emanations. In: ACM CCSGoogle Scholar
  27. Zhuang L, Zhou F, Tygar JD (2009) Keyboard acoustic emanations revisited. ACM Transactions on Information and System Security (TISSEC)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of SoftwareDalian University of TechnologyDalianChina

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