Abstract
In the context of constantly evolving carry-on technology and its increasing accessibility, namely smart-phones and tablets, a greater need for reliable authentication means comes into sight. The current study offers an alternative solution of uninterrupted testing towards verifying user legitimacy. A continuously collected dataset of 41 users’ touch-screen inputs provides a good starting point into modeling each user’s behavior and later differentiate among users. We introduce a system capable of processing features based on raw data extracted from user-screen interactions and attempting to assign each gesture to its originator. Achieving an accuracy of over 83 %, we prove that this type of authentication system is feasible and that it can be further integrated as a continuous way of disclosing intruders within given mobile applications.
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© 2015 Springer International Publishing Switzerland
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Budulan, Ş., Burceanu, E., Rebedea, T., Chiru, C. (2015). Continuous User Authentication Using Machine Learning on Touch Dynamics. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_65
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DOI: https://doi.org/10.1007/978-3-319-26532-2_65
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Publisher Name: Springer, Cham
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