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- W2890143730 abstract "Since the amount of sensitive information stored on smart-phones expands with the growth of their popularity, the need for reliable and usable authentication on these devices increases. Constant reauthentication requests of standard methods, such as PINs, passwords, and swipe patterns, annoy many users who prefer to sacrifice the security of their mobile devices for the quest for maximum usability. An alternative to this approach is sensor-based authentication, where we fingerprint the user interaction by processing signals from sensors such as touchscreen or accelerometer. In this paper, we construct a budgeted online version of One-Class Support Vector Machine (OC-SVM) to maintain authentication performance while limiting the model size and evaluate the performance compared to an unconstrained model. The results of our experiments reveal that it is possible to correctly detect invalid smartphone users in a constrained environment using our budgeted learning methodology." @default.
- W2890143730 created "2018-09-27" @default.
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- W2890143730 date "2018-04-01" @default.
- W2890143730 modified "2023-09-28" @default.
- W2890143730 title "Learning on a Budget for User Authentication on Mobile Devices" @default.
- W2890143730 doi "https://doi.org/10.1109/icassp.2018.8461898" @default.
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