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Scalable and Secure HTML5 Canvas-Based User Authentication

  • Conference paper
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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

Although browser fingerprinting has been widely studied from a privacy angle, there is also a case for fingerprinting in the context of risk-based authentication. Given that most browser-context features can be easily spoofed, APIs that potentially depend both on software and hardware have gained interest. HTML5 Canvas has been shown to provide a certain degree of characterization of a browser. However, multiple research questions remain open. In this paper, we study how to use this API for browser fingerprinting in a scalable way by means of a Siamese deep neural network. We also explore the limits of this technique on modern browsers that are progressively standardizing the Canvas outputs. On our evaluation using over 200 browser instances, we obtain an 82% accuracy in distinguishing browser instances in our dataset and 92% if the model only distinguishes between users with a different browser or OS. Our model has a 0% false-rejection rate and up to 36% average false acceptance rate on simulated attacks, that occurs mostly when victims and attackers share the same browser model and version and the same OS.

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Notes

  1. 1.

    https://coveryourtracks.eff.org/.

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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. Esteban Rivera
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  2. Lizzy Tengana
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  3. Jesús Solano
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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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Rivera, E., Tengana, L., Solano, J., López, C., Flórez, J., Ochoa, M. (2022). Scalable and Secure HTML5 Canvas-Based User 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_30

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  • DOI: https://doi.org/10.1007/978-3-031-16815-4_30

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  • Online ISBN: 978-3-031-16815-4

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