SocialAuth : Designing touch behavioral smartphone user authentication based on social networking applications

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationICT Systems Security and Privacy Protection - 34th IFIP TC 11 International Conference, SEC 2019, Proceedings
EditorsGurpreet Dhillon, Fredrik Karlsson, Karin Hedström, André Zúquete
PublisherSpringer, Cham
Pages180-193
ISBN (Electronic)978-3-030-22312-0
ISBN (Print)978-3-030-22311-3
Publication statusPublished - Jun 2019

Publication series

NameIFIP Advances in Information and Communication Technology
Volume562
ISSN (Print)1868-4238

Conference

Title34th IFIP TC 11 International Conference on Information Security and Privacy Protection, SEC 2019
PlacePortugal
CityLisbon
Period25 - 27 June 2019

Abstract

Modern smartphones expressed an exponential growth and have become a personal assistant in people’s daily lives, i.e., keeping connected with peers. Users are willing to store their personal data even sensitive information on the phones, making these devices an attractive target for cyber-criminals. Due to the limitations of traditional authentication methods like Personal Identification Number (PIN), research has been moved to the design of touch behavioral authentication on smartphones. However, how to design a robust behavioral authentication in a long-term period remains a challenge due to behavioral inconsistency. In this work, we advocate that touch gestures could become more consistent when users interact with specific applications. In this work, we focus on social networking applications and design a touch behavioral authentication scheme called SocialAuth. In the evaluation, we conduct a user study with 50 participants and demonstrate that touch behavioral deviation under our scheme could be significantly decreased and kept relatively stable even after a long-term period, i.e., a single SVM classifier could achieve an average error rate of about 3.1% and 3.7% before and after two weeks, respectively.

Research Area(s)

  • Behavioral user authentication, Machine learning, Smartphone security, Social networking, Touch gestures, Usable security

Citation Format(s)

SocialAuth : Designing touch behavioral smartphone user authentication based on social networking applications. / Meng, Weizhi; Li, Wenjuan; Jiang, Lijun; Zhou, Jianying.

ICT Systems Security and Privacy Protection - 34th IFIP TC 11 International Conference, SEC 2019, Proceedings. ed. / Gurpreet Dhillon; Fredrik Karlsson; Karin Hedström; André Zúquete. Springer, Cham, 2019. p. 180-193 (IFIP Advances in Information and Communication Technology; Vol. 562).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)