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Predicted Templates: Learning-curve Based Template Projection for Keystroke Dynamics
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Datum
2018
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Köllen Druck+Verlag GmbH
Zusammenfassung
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-error-rate
(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%.