Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Wei Han Lee, Ruby B. Lee

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

60 Scopus citations

Abstract

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.

Original languageEnglish (US)
Title of host publicationProceedings - 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages297-308
Number of pages12
ISBN (Electronic)9781538605417
DOIs
StatePublished - Aug 30 2017
Event47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2017 - Denver, United States
Duration: Jun 26 2017Jun 29 2017

Publication series

NameProceedings - 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2017

Other

Other47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2017
Country/TerritoryUnited States
CityDenver
Period6/26/176/29/17

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

Keywords

  • Authentication
  • Context
  • Machine Learning
  • Privacy
  • Security
  • Sensors
  • Smartphone
  • Smartwatch

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