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A Siamese Neural Network for Scalable Behavioral Biometrics Authentication

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Applied Cryptography and Network Security Workshops (ACNS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13285))

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Abstract

The rise in popularity of web and mobile applications brings about a need of robust authentication systems. Behavioral Biometrics Authentication has emerged as a complementary risk-based authentication approach which aims at profiling users based on their interaction with computers/smartphones. In this work we propose a novel approach based on Siamese Neural Networks to perform a few-shot verification of user’s behavior. We develop our approach to authenticate either human-computer or human-smartphone interaction. For computer interaction, our approach learns from mouse and keyboard dynamics, while for smartphone interaction it learns from holding patterns and touch patterns. The proposed approach requires only one model to authenticate all the users of a system, as opposed to the one model per user paradigm. This is a key aspect with respect to the scalability of our approach. The proposed model exhibits a few-shot classification accuracy of up to 99.8% and 90.8% for mobile and web interactions, respectively. We also test our approach on a database that contains over 100K interactions collected in the wild.

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Notes

  1. 1.

    A service where human workers perform a certain task following instructions defined by the task requester.

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Author information

Authors and Affiliations

  1. Appgate Inc., Bogotá, Colombia

    Esteban Rivera, Christian López & Johana Flórez

  2. Department of Computer Science, ETH Zürich, Zürich, Switzerland

    Martín Ochoa

  3. University College London, London, UK

    Jesús Solano

  4. Aalto University, Espoo, Finland

    Lizzy Tengana

Authors
  1. Jesús Solano
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  2. Esteban Rivera
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  3. Lizzy Tengana
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  4. Christian López
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  5. Johana Flórez
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  6. Martín Ochoa
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Corresponding author

Correspondence to Esteban Rivera .

Editor information

Editors and Affiliations

  1. Singapore University of Technology and Design, Singapore, Singapore

    Jianying Zhou

  2. University of Bristol, Bristol, UK

    Sridhar Adepu

  3. University of Malaga, Malaga, Spain

    Cristina Alcaraz

  4. Radboud University Nijmegen, Málaga, The Netherlands

    Lejla Batina

  5. Sapienza University of Rome, Rome, Roma, Italy

    Emiliano Casalicchio

  6. Singapore University of Technology and Design, Singapore, Singapore

    Sudipta Chattopadhyay

  7. Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

    Chenglu Jin

  8. University of Science and Technology of China, Hefei, China

    Jingqiang Lin

  9. University of Padua, Padua, Italy

    Eleonora Losiouk

  10. Concordia University, Montreal, QC, Canada

    Suryadipta Majumdar

  11. Technical University Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  12. Delft University of Technology, Delft, The Netherlands

    Stjepan Picek

  13. Zhejiang Gongshang University, Hangzhou, China

    Jun Shao

  14. University of Aizu, Aizu-Wakamatsu, Japan

    Chunhua Su

  15. City University of Hong Kong, Hong Kong, Hong Kong

    Cong Wang

  16. Delft University of Technology, Delft, The Netherlands

    Yury Zhauniarovich

  17. Rutgers University, Piscataway, NJ, USA

    Saman Zonouz

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Solano, J., Rivera, E., Tengana, L., López, C., Flórez, J., Ochoa, M. (2022). A Siamese Neural Network for Scalable Behavioral Biometrics Authentication. In: Zhou, J., et al. Applied Cryptography and Network Security Workshops. ACNS 2022. Lecture Notes in Computer Science, vol 13285. Springer, Cham. https://doi.org/10.1007/978-3-031-16815-4_28

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  • DOI: https://doi.org/10.1007/978-3-031-16815-4_28

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  • Online ISBN: 978-3-031-16815-4

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