On the discriminability of keystroke feature vectors used in fixed text keystroke authentication

Heterogeneous and aggregate vectors are the two widely used feature vectors in fixed text keystroke authentication. In this paper, we address the question “Which vectors, heterogeneous, aggregate, or a combination of both, are more discriminative and why?” We accomplish this in three ways – (1) by providing an intuitive example to illustrate how aggregation of features inherently reduces discriminability; (2) by formulating “discriminability” as a non-parametric estimate of Bhattacharya distance, we show theoretically that the discriminability of a heterogeneous vector is higher than an aggregate vector; and (3) by conducting user recognition experiments using a dataset containing keystrokes from 33 users typing a 32-character reference text, we empirically validate our theoretical analysis. To compare the discriminability of heterogeneous and aggregate vectors with different combinations of keystroke features, we conduct feature selection analysis using three methods: (1) ReliefF, (2) correlation based feature selection, and (3) consistency based feature selection. Results of feature selection analysis reinforce the findings of our theoretical analysis.

Research highlights
► We present a discriminability analysis of keystroke feature vectors.
► We show why heterogeneous vectors are more discriminative than aggregate vectors.
► Our theoretical analysis is validated with user recognition experiments.
► Our theoretical analysis is additionally validated with feature selection analysis.