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Protecting Your Smartphone from Theft Using Accelerometer

  • Huiyong Li
  • Jiannan Yu
  • Qian Cao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11342)

Abstract

In recent years, there have been many studies using the data generated by the built-in sensors of mobile phones for authentication and the selection of features is involved in the use of sensor data. This article discusses the method of biological feature selection by taking the mobile phone acceleration sensor as an example. 30 participants were invited to walk with their mobile phones for data collection to obtain data set 1. Several characteristics were evaluated from multiple aspects to select a number of effective features. 15 participants were invited to collect data set 2 under the condition of simulating dialy life. A feature-based authentication method is proposed and a success rate of 93.6% is obtained on data set 1. On the data set 2, 91.90% of the recognition success rate was obtained.

Keywords

Authentication Biological feature Accelerometer Feature evaluation 

Notes

Acknowledgments

This work was supported in part by National Natural Science Foundation of China (61602024, 61702018).

References

  1. 1.
    Böhmer, M., Hecht, B.J., Schöning, J., Krüger, A., Bauer, G.: Falling asleep with angry birds, Facebook and kindle: a large scale study on mobile application usage. In: Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, pp. 47–56 (2011)Google Scholar
  2. 2.
    Lee, W.-H., Lee, R.B.: Implicit sensor-based authentication of smartphone users with smartwatch. In: Proceedings of the Hardware and Architectural Support for Security and Privacy 2016, p. 9 (2016)Google Scholar
  3. 3.
    Consumer Reports 2013: Keep your phone safe: how to protect yourself from wireless threats. Consumer reports, Technical (2013)Google Scholar
  4. 4.
    Harbach, M., Von Zezschwitz, E., Fichtner, A., De Luca, A., Smith, M.: It’s a hard lock life: a field study of smartphone (un)locking behavior and risk perception. In: Symposium On Usable Privacy and Security (SOUPS 2014), pp. 213–230 (2014)Google Scholar
  5. 5.
    Shi, E., Niu, Y., Jakobsson, M., Chow, R.: Implicit authentication through learning user behavior. In: Burmester, M., Tsudik, G., Magliveras, S., Ilić, I. (eds.) ISC 2010. LNCS, vol. 6531, pp. 99–113. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-18178-8_9CrossRefGoogle Scholar
  6. 6.
    Schaub, F., Deyhle, R., Weber, M.: Password entry usability and shoulder surfing susceptibility on different smartphone platforms. In: Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia, p. 13 (2012)Google Scholar
  7. 7.
    Spencer, B.: Mobile users can’t leave their phone alone for six minutes and check it up to 150 times a day. http://www.dailymail.co.uk/news/article-2276752/Mobile-users-leave-phone-minutes-check-150-times-day.Html
  8. 8.
    Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R., Estrin, D.: Diversity in smartphone usage. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp. 179–194 (2010)Google Scholar
  9. 9.
    Weir, M., Aggarwal, S., De Medeiros, B., Glodek, B.: Password cracking using probabilistic context-free grammars. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 391–405 (2009)Google Scholar
  10. 10.
    Bonneau, J.: The science of guessing: analyzing an anonymized corpus of 70 million passwords. In: 2012 IEEE Symposium on Security and Privacy, pp. 538–552 (2012)Google Scholar
  11. 11.
    Kelley, P.G., et al.: Guess again (and again and again): measuring password strength by simulating password-cracking algorithms. In: 2012 IEEE Symposium on Security and Privacy, pp. 523–537 (2012)Google Scholar
  12. 12.
    Muaaz, M., Mayrhofer, R.: Smartphone-based gait recognition: from authentication to imitation. IEEE Trans. Mob. Comput. 16(11), 3209–3221 (2017)CrossRefGoogle Scholar
  13. 13.
    Singha, T.B., Nath, R.K., Narsimhadhan, A.V.: Person recognition using smartphones’ accelerometer data (2017)Google Scholar
  14. 14.
    Buriro, A., Crispo, B., Delfrari, F., Wrona, K.: Hold and sign: a novel behavioral biometrics for smartphone user authentication. In: 2016 IEEE Security and Privacy Workshops (SPW), pp. 276–285 (2016)Google Scholar
  15. 15.
    Sitova, Z., et al.: HMOG: new behavioral biometric features for continuous authentication of smartphone users. IEEE Trans. Inf. Forensics Secur. 11(5), 877–892 (2016)CrossRefGoogle Scholar
  16. 16.
    Zhu, H., Jingmei, H., Chang, S., Li, L.: Shakein: secure user authentication of smartphones with single-handed shakes. IEEE Trans. Mob. Comput. 16(10), 2901–2912 (2017)CrossRefGoogle Scholar
  17. 17.
    Buriro, A., Crispo, B., Del Frari, F., Wrona, K.: Touchstroke: smartphone user authentication based on touch-typing biometrics. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds.) ICIAP 2015. LNCS, vol. 9281, pp. 27–34. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23222-5_4CrossRefGoogle Scholar
  18. 18.
    Lu, Y., Wei, Y., Liu, L., Zhong, J., Sun, L., Liu, Y.: Towards unsupervised physical activity recognition using smartphone accelerometers. Multimedia Tools Appl. 76(8), 10701–10719 (2017)CrossRefGoogle Scholar
  19. 19.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Cell phone-based biometric identification. In: 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–7 (2010)Google Scholar
  20. 20.
    Alvarez, D., González, R.C., López, A., Alvarez, J.C.: Comparison of step length estimators from weareable accelerometer devices. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, vol. 1, pp. 5964–5967 (2006)Google Scholar
  21. 21.
    Sekine, M., et al.: Assessment of gait parameter in hemiplegic patients by accelerometry. In: Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No. 00CH37143), vol. 3, pp. 1879–1882 (2000)Google Scholar
  22. 22.
    Hemminki, S., Nurmi, P., Tarkoma, S.: Accelerometer-based transportation mode detection on smartphones. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, p. 13 (2013)Google Scholar
  23. 23.
    Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Using mobile phones to determine transportation modes. ACM Trans. Sensor Netw. 6(2), 13 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer ScienceBeihang UniversityBeijingChina
  2. 2.Department of Computer and Information EngineeringBeijing Technology and Business UniversityBeijingChina

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