Machine learning powered authentication challenges

    公开(公告)号:US11641368B1

    公开(公告)日:2023-05-02

    申请号:US16450463

    申请日:2019-06-24

    Applicant: Snap Inc.

    Abstract: Systems and methods are disclosed for automatically predicting a risk score of a user login attempt by receiving a user login attempt and generating a login feature vector associated with the user login attempt. The systems and methods further train a machine learning technique to establish a relationship between the login feature vector and the risk score. The trained machine learning technique is applied to new user login attempts to predict a risk score associated with the login attempt and issue an authentication challenge to the user if the risk score exceeds a predetermined threshold value.

    MACHINE LEARNING POWERED AUTHENTICATION CHALLENGES

    公开(公告)号:US20230262082A1

    公开(公告)日:2023-08-17

    申请号:US18303807

    申请日:2023-04-20

    Applicant: Snap Inc.

    CPC classification number: H04L63/1425 G06N20/00 H04L63/083 H04L63/1433

    Abstract: Systems and methods are disclosed for automatically predicting a risk score of a user login attempt by receiving a user login attempt and generating a login feature vector associated with the user login attempt. The systems and methods further train a machine learning technique to establish a relationship between the login feature vector and the risk score. The trained machine learning technique is applied to new user login attempts to predict a risk score associated with the login attempt and issue an authentication challenge to the user if the risk score exceeds a predetermined threshold value.

    Machine learning powered authentication challenges

    公开(公告)号:US12088613B2

    公开(公告)日:2024-09-10

    申请号:US18303807

    申请日:2023-04-20

    Applicant: Snap Inc.

    CPC classification number: H04L63/1425 G06N20/00 H04L63/083 H04L63/1433

    Abstract: Systems and methods are disclosed for automatically predicting a risk score of a user login attempt by receiving a user login attempt and generating a login feature vector associated with the user login attempt. The systems and methods further train a machine learning technique to establish a relationship between the login feature vector and the risk score. The trained machine learning technique is applied to new user login attempts to predict a risk score associated with the login attempt and issue an authentication challenge to the user if the risk score exceeds a predetermined threshold value.

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