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2017 | OriginalPaper | Buchkapitel

Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning

verfasst von : Lichao Sun, Yuqi Wang, Bokai Cao, Philip S. Yu, Witawas Srisa-an, Alex D. Leow

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

With the rapid growth in smartphone usage, more organizations begin to focus on providing better services for mobile users. User identification can help these organizations to identify their customers and then cater services that have been customized for them. Currently, the use of cookies is the most common form to identify users. However, cookies are not easily transportable (e.g., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification. In this paper, we propose DeepService, a new technique that can identify mobile users based on user’s keystroke information captured by a special keyboard or web browser. Our evaluation results indicate that DeepService is highly accurate in identifying mobile users (over 93% accuracy). The technique is also efficient and only takes less than 1 ms to perform identification.

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Titel: Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning
verfasst von: Lichao Sun
Yuqi Wang
Bokai Cao
Philip S. Yu
Witawas Srisa-an
Alex D. Leow
Verlag: Springer International Publishing
Buch: Machine Learning and Knowledge Discovery in Databases
Print ISBN: 978-3-319-71272-7

Electronic ISBN: 978-3-319-71273-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-71273-4_19

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