Learning on a Budget for User Authentication on Mobile Devices

Bojan Kolosnjaji*, Antonia Hüfner, Claudia Eckert, Apostolis Zarras

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Since the amount of sensitive information stored on smartphones 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.
Original languageEnglish
Title of host publicationProceeding of the 43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
ISBN (Print)9781538646588
Publication statusPublished - 2018
EventIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - CANADA
Duration: 15 Apr 201820 Apr 2018


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)


  • Machine Learning
  • User Authentication
  • Security

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