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Friend or Foe?: Your Wearable Devices Reveal Your Personal PIN

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Published:30 May 2016Publication History

ABSTRACT

The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80% accuracy with only one try and more than 90% accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information.

References

  1. All about skimmers. http://krebsonsecurity.com/all-about-skimmers/.Google ScholarGoogle Scholar
  2. Is it acceptable to wear a watch on the right wrist? http://www.askandyaboutclothes.com/forum/showthread.php? 116570-Is-it-acceptable-to-wear-a-watch-on-the-right-wrist.Google ScholarGoogle Scholar
  3. Malicious cloned games attack google android market. naked security:. http://nakedsecurity.sophos.com/2011/12/12/ malicious-cloned-games-attack-google-android-market/.Google ScholarGoogle Scholar
  4. Wearable device shipments predicted to surge 173% this year. http://www.cnet.com/news/shipments-of-wearable-device-to-surge-173-this-year/.Google ScholarGoogle Scholar
  5. Why wear a watch on the wrist where you're hand dominant. http://www.reddit.com/r/Watches/comments/1wzub5/question_why_wear_a_watch_on_the_wrist_where/.Google ScholarGoogle Scholar
  6. D. Balzarotti, M. Cova, and G. Vigna. Clearshot: Eavesdropping on keyboard input from video. In IEEE S&P, pages 170--183, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Y. Berger, A. Wool, and A. Yeredor. Dictionary attacks using keyboard acoustic emanations. In ACM CCS, pages 245--254, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. Liu, Y. Wang, k. Kar, Y. Chen, J. Yang, and M. Gruteser. Snooping keystrokes with mm-level audio ranging on a single phone. In ACM Mobicom, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. Liu and et al. Toward detection of unsafe driving with wearables. In ACM WearSys, pages 27--32, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. X. Liu, Z. Zhou, W. Diao, Z. Li, and K. Zhang. When good becomes evil: Keystroke inference with smartwatch. In ACM CCS, pages 1273--1285, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. F. Maggi, A. Volpatto, S. Gasparini, G. Boracchi, and S. Zanero. A fast eavesdropping attack against touchscreens. In IEEE IAS, pages 320--325, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  12. P. Marquardt, A. Verma, H. Carter, and P. Traynor. (sp)iphone: decoding vibrations from nearby keyboards using mobile phone accelerometers. In ACM CCS, pages 551--562, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. E. Miluzzo, A. Varshavsky, S. Balakrishnan, and R. R. Choudhury. Tapprints: your finger taps have fingerprints. In ACM MobiSys, pages 323--336, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. Parate and et al. RisQ: recognizing smoking gestures with inertial sensors on a wristband. In ACM MobiSys, pages 149--161, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Y. Ren, Y. Chen, M. C. Chuah, and J. Yang. User verification leveraging gait recognition for smartphone enabled mobile healthcare systems. IEEE Transactions on Mobile Computing, 2014.Google ScholarGoogle Scholar
  16. M. Ryan. Bluetooth: With low energy comes low security. In USENIX WOOT, pages 4--4, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. M. Sherman and et al. User-generated free-form gestures for authentication: Security and memorability. In ACM Mobisys, pages 176--189, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. D. Shukla, R. Kumar, A. Serwadda, and V. V. Phoha. Beware, your hands reveal your secrets! In ACM CCS, pages 904--917, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. Spill and A. Bittau. Bluesniff: Eve meets alice and bluetooth. In USENIX WOOT, pages 5:1--5:10, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. H. Wang, T. T.-T. Lai, and R. Roy Choudhury. Mole: Motion leaks through smartwatch sensors. In ACM MobiCom, pages 155--166, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. J. Wang, K. Zhao, X. Zhang, and C. Peng. Ubiquitous keyboard for small mobile devices: Harnessing multipath fading for fine-grained keystroke localization. In ACM Mobysis, pages 14--27, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Z. Xu, K. Bai, and S. Zhu. Taplogger: Inferring user inputs on smartphone touchscreens using on-board motion sensors. In ACM WISEC, pages 113--124, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. T. Zhu, Q. Ma, S. Zhang, and Y. Liu. Context-free attacks using keyboard acoustic emanations. In ACM CCS, pages 453--464, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Conferences
            ASIA CCS '16: Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security
            May 2016
            958 pages
            ISBN:9781450342339
            DOI:10.1145/2897845

            Copyright © 2016 ACM

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            Publication History

            • Published: 30 May 2016

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            ASIA CCS '16 Paper Acceptance Rate73of350submissions,21%Overall Acceptance Rate418of2,322submissions,18%

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