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Protecting Your Smartphone from Theft Using Accelerometer

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11342))

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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.

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Notes

  1. 1.

    https://www.cs.waikato.ac.nz/ml/weka/.

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Acknowledgments

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

Author information

Authors and Affiliations

  1. School of Computer Science, Beihang University, Beijing, 100000, China

    Huiyong Li & Jiannan Yu

  2. Department of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100000, China

    Qian Cao

Authors
  1. Huiyong Li
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  2. Jiannan Yu
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  3. Qian Cao
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Corresponding authors

Correspondence to Huiyong Li , Jiannan Yu or Qian Cao .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Swinburne University of Technology, Melbourne, VIC, Australia

    Jinjun Chen

  3. St. Francis Xavier University, Antigonish, NS, Canada

    Laurence T. Yang

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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

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  • DOI: https://doi.org/10.1007/978-3-030-05345-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05344-4

  • Online ISBN: 978-3-030-05345-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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