Fast Free-text Authentication via Instance-based Keystroke Dynamics
This work addresses the need for faster and more efficient user authentication in behavioral biometric systems, representing a strong specific gain rather than a foundational advancement.
The paper tackled the problem of reducing the number of keystrokes needed for free-text keystroke dynamics authentication by proposing a novel instance-based graph comparison algorithm (ITAD metric) and fusing features, achieving equal error rates of 9.7% for 100 digraphs and 7.8% for 200 digraphs on the Clarkson II dataset, significantly improving upon previous state-of-the-art rates of 35.3% and 15.3%.
Keystroke dynamics study the way in which users input text via their keyboards. Having the ability to differentiate users, typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve existing account security. Keystroke dynamics systems on free-text data have previously required 500 or more characters to achieve reasonable performance. In this paper, we propose a novel instance-based graph comparison algorithm called the instance-based tail area density (ITAD) metric to reduce the number of keystrokes required to authenticate users. Additionally, commonly used features in the keystroke dynamics literature, such as monographs and digraphs, are all found to be useful in informing who is typing. The usefulness of these features for authentication is determined using a random forest classifier and validated across two publicly available datasets. Scores from the individual features are fused to form a single matching score. With the fused matching score and our ITAD metric, we achieve equal error rates (EERs) for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset, improving upon state-of-the-art of 35.3% and 15.3%.