VeriNet: User Verification on Smartwatches via Behavior Biometrics

Chris Xiaoxuan Lu, Bowen Du, Xuan Kan, Hongkai Wen, Andrew Markham, Niki Trigoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

No longer reserved for nerdy geeks, nowadays smartwatches have gain their popularities rapidly, and become one of the most desirable gadgets that the general public would like to own. However, such popularity also introduces potential vulnerability. Until now, the de facto solution to protect smartwatches are passwords, i.e. either PINs or Android Pattern Locks (APLs). Unfortunately, those types of passwords are not robust against various forms of attacks, such as shoulder surfing or touch/motion based side channel attacks. In this paper, we propose a novel authentication approach for smartwatches, which adds another layer of security on top of the traditional passwords by considering the unique motion signatures when different users input passwords on their watches. It uses a deep recurrent neural networks to analyse the subtle motion signals of password input, and distinguish the legitimate users from malicious impostors. Following a privacy-preserving manner, our proposed approach does not require users to upload their passcodes for model training but only the motion data and identity labels. Extensive experiments on large-scale datasets collected real-world show that the proposed approach outperforms the state-of-the-art significantly, even in the most challenging case where a user has multiple distinct passcodes.
Original languageEnglish
Title of host publicationProceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications
Place of PublicationNew York, NY, USA
PublisherACM Association for Computing Machinery
Pages68–73
Number of pages6
ISBN (Print)9781450355551
DOIs
Publication statusPublished - 6 Nov 2017
EventFirst ACM Workshop on Mobile Crowdsensing Systems and Applications - Delft, Netherlands
Duration: 6 Nov 20176 Nov 2017
https://webhome.cs.uvic.ca/~wkui/crowdsense/index.html

Publication series

NameCrowdSenSys '17
PublisherAssociation for Computing Machinery

Workshop

WorkshopFirst ACM Workshop on Mobile Crowdsensing Systems and Applications
Abbreviated titleCrowdSenSys 2017
CountryNetherlands
CityDelft
Period6/11/176/11/17
Internet address

Keywords

  • Motion Sensors
  • Smartwatch
  • PINs

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