Keystroke Dynamics for User Identification

• URL: http://arxiv.org/abs/2307.05529v1
• Date: Fri, 7 Jul 2023 23:12:16 GMT
• Title: Keystroke Dynamics for User Identification
• Authors: Atharva Sharma and Martin Jure\v{c}ek and Mark Stamp
• Abstract summary: In previous research, keystroke dynamics has shown promise for user authentication, based on both fixed-text and free-text data. We consider the more challenging multiclass user identification problem, based on free-text data.
• Score: 2.707154152696381
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In previous research, keystroke dynamics has shown promise for user authentication, based on both fixed-text and free-text data. In this research, we consider the more challenging multiclass user identification problem, based on free-text data. We experiment with a complex image-like feature that has previously been used to achieve state-of-the-art authentication results over free-text data. Using this image-like feature and multiclass Convolutional Neural Networks, we are able to obtain a classification (i.e., identification) accuracy of 0.78 over a set of 148 users. However, we find that a Random Forest classifier trained on a slightly modified version of this same feature yields an accuracy of 0.93.

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