Multi-factor authentication for wearables: Configuring system parameters with risk function

Sergey Bezzateev, Aleksandra Afanasyeva, Natalia Voloshina, Aleksandr Ometov

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

    2 Citations (Scopus)
    1 Downloads (Pure)

    Abstract

    The users of today are already about to enter the era of highly integrated modern wearable devices-the time when smart accessorieswill, in turn, push aside regular Smartphones and Tablets bringinga variety of new security challenges. The number of simultaneously used bio-sensors, both integrated into smart wearables andconnected over wireless interfaces, allows novel opportunities forMulti-factor Authentication (MFA) of the user. flis manuscriptproposes a solution for conflguring the MFA based on the averagedirect and indirect losses risk analysis. the example applicationof Bayesian function for MFA presents the applicability of the proposed framework for the utilization with wearables.

    Original languageEnglish
    Title of host publicationProceedings of the 2nd International Conference on Advanced Wireless Information, Data, and Communication Technologies, AWICT 2017
    PublisherACM
    ISBN (Electronic)9781450353106
    DOIs
    Publication statusPublished - 13 Nov 2017
    Publication typeA4 Article in conference proceedings
    EventInternational Conference on Advanced Wireless Information, Data, and Communication Technologies - Paris, France
    Duration: 13 Nov 201714 Nov 2017

    Conference

    ConferenceInternational Conference on Advanced Wireless Information, Data, and Communication Technologies
    Country/TerritoryFrance
    CityParis
    Period13/11/1714/11/17

    Keywords

    • Information security
    • Multi-factor authentication
    • Risk function
    • Wearables

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Computer Networks and Communications
    • Computer Vision and Pattern Recognition
    • Software

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