A study of biometric feature for a recall-based behavioral graphical mobile authentication

Jing Chiou Liou, Andrew Scaduto

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

Abstract

With the increasing use of mobile devices, including smart phones and tablets, in today's IT infrastructure, the paradigms of computer applications have been significantly changed. One area of studies is to utilize a new authentication scheme that will provide a better protection on access control and privacy of the mobile device user. Currently, most of the mobile devices have implemented scheme such as biometric or graphical user authentication. However, study shows people still comfortably use password code as the primary mean for authentication, even it is proven unsecure. In this paper, we propose a graphical mobile authentication scheme that integrates the psychologically popular passcode into a behavioral biometric authentication. This authentication scheme provides an easy transition from a simple passcode to a more secure recall-based graphical mode and helps to prevent shoulder surfing that many graphical authentications suffered from.

Original languageEnglish
Title of host publicationProceedings of the 3rd Multidisciplinary International Social Networks Conference, SocialInformatics 2016, Data Science 2016, MISNC, SI, DS 2016
PublisherAssociation for Computing Machinery
ISBN (Print)9781450341295
DOIs
StatePublished - 15 Aug 2016
Event3rd Multidisciplinary International Social Networks Conference, MISNC 2016, 5th ASE International Conference on Social Informatics, SocialInformatics 2016 and 7th ASE International Conference on Data Science, DS 2016 - Union, United States
Duration: 15 Aug 201617 Aug 2016

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd Multidisciplinary International Social Networks Conference, MISNC 2016, 5th ASE International Conference on Social Informatics, SocialInformatics 2016 and 7th ASE International Conference on Data Science, DS 2016
Country/TerritoryUnited States
CityUnion
Period15/08/1617/08/16

Keywords

  • Behavioral biometric
  • Dynamic authentication
  • Mobile computing
  • Multi-modal biometric

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