Learning on a Budget for User Authentication on Mobile Devices
Bojan Kolosnjaji, Antonia Hufner, Claudia Eckert, Apostolis Zarras
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.
BibTeX
@inproceedings{icassp2018_learningonabudge,
title = {Learning on a Budget for User Authentication on Mobile Devices},
author = {Bojan Kolosnjaji and Antonia Hufner and Claudia Eckert and Apostolis Zarras},
booktitle = {ICASSP 2018},
year = {2018}
}