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Cost-Effective Modeling for Authentication and Its Application to Activity Tracker

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Book cover Information Security Applications (WISA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9503))

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Abstract

The growing popularity of multi-factor authentication, it makes the need for more cost-effective approach. The more authentication factors the system use, the higher cost for machine learning all of factors it require. We considered the behavioral authentication that is brought to attention as one of the authentication factors. The behavioral authentication works well with machine learning approaches. However, with machine learning, a model must be created, and the service provider must analyze each user individually; both adding to the cost. In this paper, we propose a cost-effective user modeling approach that uses a FuelBand to obtain activity information for behavioral authentication. This approach uses a clustering method that focuses on the characteristics of our behavioral authentication method. The performance of our system was compared to that of machine learning (70 users), and for no more than half the cost, the results had an accuracy of 89.28 %.

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Acknowledgments

We would like to thank Mitsubishi UFJ NICOS Co., Ltd. for a grant that made it possible to complete this work.

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Authors and Affiliations

  1. The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Hiroya Susuki & Rie Shigetomi Yamaguchi

Authors
  1. Hiroya Susuki
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  2. Rie Shigetomi Yamaguchi
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Corresponding author

Correspondence to Hiroya Susuki .

Editor information

Editors and Affiliations

  1. Pusan National University, Busan, Korea (Republic of)

    Ho-won Kim

  2. Electronics and Telecommunications Research Institute, Daejeon, Korea (Republic of)

    Dooho Choi

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Susuki, H., Yamaguchi, R.S. (2016). Cost-Effective Modeling for Authentication and Its Application to Activity Tracker. In: Kim, Hw., Choi, D. (eds) Information Security Applications. WISA 2015. Lecture Notes in Computer Science(), vol 9503. Springer, Cham. https://doi.org/10.1007/978-3-319-31875-2_31

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  • DOI: https://doi.org/10.1007/978-3-319-31875-2_31

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31874-5

  • Online ISBN: 978-3-319-31875-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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