Keystroke dynamics-based user authentication using freely typed text based on user-adaptive feature extraction and novelty detection

Junhong Kim, Haedong Kim, Pilsung Kang

    Research output: Contribution to journalArticlepeer-review

    61 Citations (Scopus)

    Abstract

    Keystroke dynamics has been used to strengthen password-based user authentication systems by considering the typing characteristics of legitimate users. The main problem with login-based authentication systems is that they cannot authenticate users after login access is granted. To ensure continuous user authentication, keystroke dynamics collected from freely typed text during the login period has been utilized; however, the authentication performance was unsatisfactory. To enhance the performance of user authentication based on freely typed keystrokes, we propose a user-adaptive feature extraction method that captures individual users’ distinctive typing behaviors embedded in relative typing speeds for different digraphs. Based on experimental results obtained from 150 participants with more than 13,000 keystrokes per each user in two languages (Korean and English), the proposed method achieved the best equal error rate (0.44). Furthermore, the authentication performance was enhanced by 45.3% for Korean and 39.0% for English compared with the benchmark fixed feature extraction method.

    Original languageEnglish
    Pages (from-to)1077-1087
    Number of pages11
    JournalApplied Soft Computing Journal
    Volume62
    DOIs
    Publication statusPublished - 2018 Jan

    Bibliographical note

    Publisher Copyright:
    © 2017 Elsevier B.V.

    Keywords

    • Free text
    • Keystroke dynamics
    • Novelty detection
    • User authentication
    • User-adaptive features

    ASJC Scopus subject areas

    • Software

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