User authentication via keystroke dynamics based on difference subspace and slope correlation degree

User authentication via keystroke dynamics remains a challenging problem due to the fact that keystroke dynamics pattern cannot be maintained stable over time. This paper describes a novel keystroke dynamics-based user authentication approach. The proposed approach consists of two stages, a training stage and an authentication stage. In the training stage, a set of orthogonal bases and a common feature vector are periodically generated from keystroke features of a legitimate user?s several recent successful authentications. In the authentication stage, the current keystroke feature vector is projected onto the set of orthogonal bases, and the distortion of the feature vector between its projection is obtained. User authentication is implemented by comparing the slope correlation degree of the distortion between the common feature vector with a threshold determined periodically using the recent impostor patterns. Theoretical and experimental results show that the proposed method presents high tolerance to instability of user keystroke patterns and yields better performance in terms of false acceptance rate (FAR) and false rejection rate (FRR) compared with some recent methods.