An Evaluation of One-Class and Two-Class Classification Algorithms for Keystroke Dynamics Authentication on Mobile Devices

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Abstract
In this paper we study keystroke dynamics as an authentication mechanism for touch screen based devices. The authentication process decides whether the identity of a given person is accepted or rejected. This can be easily implemented by using a two-class classifier which operates with the help of positive samples (belonging to the authentic person) and negative ones. However, collecting negative samples is not always a viable option. In such cases a one-class classification algorithm can be used to characterize the target class and distinguish it from the outliers. We implemented an authentication test-framework that is capable of working with both one-class and two-class classification algorithms. The framework was evaluated on our dataset containing keystroke samples from 42 users, collected from touch screen-based Android devices. Experimental results yield an Equal Error Rate (EER) of 3% (two-class) and 7% (one-class) respectively.

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