Secure Pick Up: Implicit authentication when you start using the smartphone

Wei Han Lee, Xiaochen Liu, Yilin Shen, Hongxia Jin, Ruby B. Lee

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

42 Scopus citations

Abstract

We propose Secure Pick Up (SPU), a convenient, lightweight, indevice, non-intrusive and automatic-learning system for smartphone user authentication. Operating in the background, our system implicitly observes users' phone pick-up movements, the way they bend their arms when they pick up a smartphone to interact with the device, to authenticate the users. Our SPU outperforms the state-of-the-art implicit authentication mechanisms in three main aspects: 1) SPU automatically learns the user's behavioral pattern without requiring a large amount of training data (especially those of other users) as previous methods did, making it more deployable. Towards this end, we propose a weighted multi-dimensional Dynamic Time Warping (DTW) algorithm to effectively quantify similarities between users' pick-up movements; 2) SPU does not rely on a remote server for providing further computational power, making SPU efficient and usable even without network access; and 3) our system can adaptively update a user's authentication model to accommodate user's behavioral drift over time with negligible overhead. Through extensive experiments on real world datasets, we demonstrate that SPU can achieve authentication accuracy up to 96.3% with a very low latency of 2.4 milliseconds. It reduces the number of times a user has to do explicit authentication by 32.9%, while effectively defending against various attacks.

Original languageEnglish (US)
Title of host publicationSACMAT 2017 - Proceedings of the 22nd ACM Symposium on Access Control Models and Technologies
PublisherAssociation for Computing Machinery
Pages67-78
Number of pages12
ISBN (Electronic)9781450347020
DOIs
StatePublished - Jun 7 2017
Event22nd ACM Symposium on Access Control Models and Technologies, SACMAT 2017 - Indianapolis, United States
Duration: Jun 21 2017Jun 23 2017

Publication series

NameProceedings of ACM Symposium on Access Control Models and Technologies, SACMAT
VolumePart F128644

Other

Other22nd ACM Symposium on Access Control Models and Technologies, SACMAT 2017
Country/TerritoryUnited States
CityIndianapolis
Period6/21/176/23/17

All Science Journal Classification (ASJC) codes

  • Software
  • Safety, Risk, Reliability and Quality
  • Information Systems
  • Computer Networks and Communications

Keywords

  • Authentication
  • Dynamic time warping
  • Machine learning
  • Mobile system
  • Privacy
  • Security
  • Smartphone

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