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An Exploration of Machine Learning Methods for Biometric Identification Based on Keystroke Dynamics
Abstract
In this chapter, authors explore keystroke dynamics as behavioral biometrics and effectiveness of state-of-the-art machine learning algorithms for identifying and authenticating users based on keystroke data. One of the motivations is to explore the use of classifiers to the field keystroke dynamics. In different settings, recent machine learning models have been effective with limited data and computationally relatively inexpensive. Therefore, authors conducted experiments with two different keystroke dynamics datasets with limited data. They demonstrated the effectiveness of models on dataset obtained from touch screen devices (mobile phones) and also on normal keyboard. Although there are similar recent studies which explore different classification algorithms, their main aim has been anomaly detection. But authors experimented with classification methods for user identification and authentication using two different keystroke datasets from touchscreens and keyboards.
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