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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
Consumer Reports 2013: Keep your phone safe: how to protect yourself from wireless threats. Consumer reports, Technical (2013)
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)
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_9
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)
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
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)
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)
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)
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)
Muaaz, M., Mayrhofer, R.: Smartphone-based gait recognition: from authentication to imitation. IEEE Trans. Mob. Comput. 16(11), 3209–3221 (2017)
Singha, T.B., Nath, R.K., Narsimhadhan, A.V.: Person recognition using smartphones’ accelerometer data (2017)
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)
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)
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)
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_4
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)
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)
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)
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)
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)
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)
Acknowledgments
This work was supported in part by National Natural Science Foundation of China (61602024, 61702018).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, H., Yu, J., Cao, Q. (2018). Protecting Your Smartphone from Theft Using Accelerometer. In: Wang, G., Chen, J., Yang, L. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2018. Lecture Notes in Computer Science(), vol 11342. Springer, Cham. https://doi.org/10.1007/978-3-030-05345-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-05345-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05344-4
Online ISBN: 978-3-030-05345-1
eBook Packages: Computer ScienceComputer Science (R0)