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SwipeVLock: A Supervised Unlocking Mechanism Based on Swipe Behavior on Smartphones

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Machine Learning for Cyber Security (ML4CS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11806))

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Abstract

Smartphones have become a necessity in people’s daily lives, and changed the way of communication at any time and place. Nowadays, mobile devices especially smartphones have to store and process a large amount of sensitive information, i.e., from personal to financial and professional data. For this reason, there is an increasing need to protect the devices from unauthorized access. In comparison with the traditional textual password, behavioral authentication can verify current users in a continuous way, which can complement the existing authentication mechanisms. With the advanced capability provided by current smartphones, users can perform various touch actions to interact with their devices. In this work, we focus on swipe behavior and aim to design a machine learning-based unlock scheme called SwipeVLock, which verifies users based on their way of swiping the phone screen with a background image. In the evaluation, we measure several typical supervised learning algorithms and conduct a user study with 30 participants. Our experimental results indicate that participants could perform well with SwipeVLock, i.e., with a success rate of 98% in the best case.

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Acknowledgments

We would like to thank the participants for their hard work in the user study. This work was partially supported by National Natural Science Foundation of China (No. 61802077).

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Authors and Affiliations

  1. School of Computer Science, Guangzhou University, Guangzhou, China

    Wenjuan Li, Weizhi Meng, Yu Wang & Jing Li

  2. Department of Computer Science, City University of Hong Kong, Kowloon, China

    Wenjuan Li

  3. KOTO Research Center, Macao, China

    Jiao Tan

  4. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark

    Weizhi Meng

Authors
  1. Wenjuan Li
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  3. Weizhi Meng
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  4. Yu Wang
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  5. Jing Li
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Corresponding author

Correspondence to Weizhi Meng .

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Editors and Affiliations

  1. Xidian University, Xi’an, China

    Xiaofeng Chen

  2. Fujian Normal University, Fuzhou, China

    Xinyi Huang

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Jun Zhang

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Li, W., Tan, J., Meng, W., Wang, Y., Li, J. (2019). SwipeVLock: A Supervised Unlocking Mechanism Based on Swipe Behavior on Smartphones. In: Chen, X., Huang, X., Zhang, J. (eds) Machine Learning for Cyber Security. ML4CS 2019. Lecture Notes in Computer Science(), vol 11806. Springer, Cham. https://doi.org/10.1007/978-3-030-30619-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-30619-9_11

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