Predicted Templates: Learning-curve Based Template Projection for Keystroke Dynamics

Ali Khodabakhsh, Erwin Haasnoot, Patrick Bours

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

    Abstract

    Keystroke Dynamics (KD) as a biometric modality can provide authentication tools in many real-life applications, virtually at zero-cost on the client side, due to the reliance of these techniques on existing hardware, and their low computational expense. One promising application is the use of KD as a second factor in password-based authentication. A downside of the existing modeling methods is the assumption of stationary behavior from the clients. However, it is expected that humans show improvements in performing a specific task following practice. In this study, we propose methods for utilization of learning models in predicting the future behavior of the clients, even with little enrollment data, and generate predicted behavioral models that can be used in different classifiers. In our experiments, the predicted templates show a reduction in the average equal-errorrate (EER) consistently across different classifiers a benchmark dataset. A reduction of 20% is achieved on the best classifier. Given fewer enrollment data, the performance gain was shown to reach above 30%. Furthermore, we show that blind detection of attacks is possible, solely relying on the global learning curve, with an EER of 16%.

    Original languageEnglish
    Title of host publication2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018
    EditorsArslan Bromme, Andreas Uhl, Christoph Busch, Christian Rathgeb, Antitza Dantcheva
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    ISBN (Electronic)9783885796763
    DOIs
    Publication statusPublished - 10 Oct 2018
    Event17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018 - Darmstadt, Germany
    Duration: 26 Sep 201828 Sep 2018
    Conference number: 17

    Publication series

    NameInternational Conference of the Biometrics Special Interest Group (BIOSIG)
    PublisherIEEE
    Volume2018
    ISSN (Electronic)1617-5468

    Conference

    Conference17th International Conference of the Biometrics Special Interest Group, BIOSIG 2018
    Abbreviated titleBIOSIG 2018
    CountryGermany
    CityDarmstadt
    Period26/09/1828/09/18

    Keywords

    • Keystroke biometrics
    • Keystroke dynamics
    • Learning curve
    • Predicted template

    Fingerprint Dive into the research topics of 'Predicted Templates: Learning-curve Based Template Projection for Keystroke Dynamics'. Together they form a unique fingerprint.

    Cite this