skip to main content
10.1145/2789168.2790109acmconferencesArticle/Chapter ViewAbstractPublication PagesmobicomConference Proceedingsconference-collections
research-article

Keystroke Recognition Using WiFi Signals

Authors Info & Claims
Published:07 September 2015Publication History

ABSTRACT

Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of Channel State Information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal based keystroke recognition system called WiKey. WiKey consists of two Commercial Off-The-Shelf (COTS) WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves more than 97.5\% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%.

References

  1. Dmitri Asonov and Rakesh Agrawal. Keyboard acoustic emanations. In 2012 IEEE Symposium on Security and Privacy, pages 3--3. IEEE Computer Society, 2004.Google ScholarGoogle Scholar
  2. Li Zhuang, Feng Zhou, and J Doug Tygar. Keyboard acoustic emanations revisited. ACM Transactions on Information and System Security (TISSEC), 13(1):3, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Tong Zhu, Qiang Ma, Shanfeng Zhang, and Yunhao Liu. Context-free attacks using keyboard acoustic emanations. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pages 453--464. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Martin Vuagnoux and Sylvain Pasini. Compromising electromagnetic emanations of wired and wireless keyboards. In USENIX Security Symposium, pages 1--16, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Davide Balzarotti, Marco Cova, and Giovanni Vigna. Clearshot: Eavesdropping on keyboard input from video. In Security and Privacy, 2008. SP 2008. IEEE Symposium on, pages 170--183. IEEE, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Chunmei Han, Kaishun Wu, Yuxi Wang, and Lionel M Ni. Wifall: Device-free fall detection by wireless networks. In INFOCOM, 2014 Proceedings IEEE, pages 271--279. IEEE, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  7. Yan Wang, Jian Liu, Yingying Chen, Marco Gruteser, Jie Yang, and Hongbo Liu. E-eyes: device-free location-oriented activity identification using fine-grained wifi signatures. In Proceedings of the 20th annual international conference on Mobile computing and networking, pages 617--628. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Zimu Zhou, Zheng Yang, Chenshu Wu, Longfei Shangguan, and Yunhao Liu. Towards omnidirectional passive human detection. In INFOCOM, 2013 Proceedings IEEE, pages 3057--3065. IEEE, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  9. Wei Xi, Jizhong Zhao, Xiang-Yang Li, Kun Zhao, Shaojie Tang, Xue Liu, and Zhiping Jiang. Electronic frog eye: Counting crowd using wifi. In INFOCOM, 2014 Proceedings IEEE, pages 361--369, April 2014.Google ScholarGoogle ScholarCross RefCross Ref
  10. Guanhua Wang, Yongpan Zou, Zimu Zhou, Kaishun Wu, and Lionel M Ni. We can hear you with wi-fi! In Proceedings of the 20th annual international conference on Mobile computing and networking, pages 593--604. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Stephan Sigg, Shuyu Shi, Felix Buesching, Yusheng Ji, and Lars Wolf. Leveraging rf-channel fluctuation for activity recognition: Active and passive systems, continuous and rssi-based signal features. In Proceedings of International Conference on Advances in Mobile Computing & Multimedia, page 43. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Stephan Sigg, Markus Scholz, Shuyu Shi, Yusheng Ji, and Michael Beigl. Rf-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. Mobile Computing, IEEE Transactions on, 13(4):907--920, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Rajalakshmi Nandakumar, Bryce Kellogg, and Shyamnath Gollakota. Wi-fi gesture recognition on existing devices. arXiv preprint arXiv:1411.5394, 2014.Google ScholarGoogle Scholar
  14. Souvik Sen, Jeongkeun Lee, Kyu-Han Kim, and Paul Congdon. Avoiding multipath to revive inbuilding wifi localization. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services, pages 249--262. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Jiang Xiao, Kaishun Wu, Youwen Yi, and Lionel M Ni. Fifs: Fine-grained indoor fingerprinting system. In Computer Communications and Networks (ICCCN), 2012 21st International Conference on, pages 1--7. IEEE, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  16. Zheng Yang, Zimu Zhou, and Yunhao Liu. From rssi to csi: Indoor localization via channel response. ACM Computing Surveys (CSUR), 46(2):25, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Qifan Pu, Sidhant Gupta, Shyamnath Gollakota, and Shwetak Patel. Whole-home gesture recognition using wireless signals. In Proceedings of the 19th annual international conference on Mobile computing & networking, pages 27--38. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Bryce Kellogg, Vamsi Talla, and Shyamnath Gollakota. Bringing gesture recognition to all devices. In Usenix NSDI, volume 14, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Bastien Lyonnet, Cornel Ioana, and Moeness G Amin. Human gait classification using microdoppler time-frequency signal representations. In Radar Conference, 2010 IEEE, pages 915--919. IEEE, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  20. Fadel Adib, Zach Kabelac, Dina Katabi, and Robert C Miller. 3d tracking via body radio reflections. In Usenix NSDI, volume 14, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Bo Chen, Vivek Yenamandra, and Kannan Srinivasan. Tracking keystrokes using wireless signals. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, pages 31--44. ACM, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Donald G Childers, David P Skinner, and Robert C Kemerait. The cepstrum: A guide to processing. Proceedings of the IEEE, 65(10):1428--1443, 1977.Google ScholarGoogle ScholarCross RefCross Ref
  23. Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall. 802.11 with multiple antennas for dummies. ACM SIGCOMM Computer Communication Review, 40(1):19--25, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall. Tool release: gathering 802.11 n traces with channel state information. ACM SIGCOMM Computer Communication Review, 41(1):53--53, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Meinard Müller. Dynamic time warping. Information retrieval for music and motion, pages 69--84, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  26. Jyh-Shing Roger Jang. Machine learning toolbox, available at http://mirlab.org/jang/matlab/toolbox/machinelearning, accessed on december 23, 2014.Google ScholarGoogle Scholar
  27. Neal Patwari and Sneha K Kasera. Robust location distinction using temporal link signatures. In Proceedings of the 13th annual ACM international conference on Mobile computing and networking, pages 111--122. ACM, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Neal Patwari and Sneha K Kasera. Temporal link signature measurements for location distinction. Mobile Computing, IEEE Transactions on, 10(3):449--462, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Keystroke Recognition Using WiFi Signals

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        MobiCom '15: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking
        September 2015
        638 pages
        ISBN:9781450336192
        DOI:10.1145/2789168

        Copyright © 2015 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]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 September 2015

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        MobiCom '15 Paper Acceptance Rate38of207submissions,18%Overall Acceptance Rate440of2,972submissions,15%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      ePub

      View this article in ePub.

      View ePub