Non-Conventional Keystroke Dynamics for User Authentication

This paper introduces an approach for user authentication using free-text keystroke dynamics which incorporates the use of non-conventional keystroke features. Semi-timing features along with editing features are extracted from the users’ typing stream. Decision trees were exploited to classify each of the users’ data. In parallel for comparison, Support Vector Machines (SVMs) were also used for classification in association with an Ant Colony Optimization (ACO) feature selection technique. The results obtained from this study are encouraging as low False Accept Rates (FAR) and False Reject Rates (FRR) were achieved in the experimentation phase. This signifies that satisfactory overall system performance was achieved by using the typing attributes in the proposed approach. Thus, the use of non-conventional typing features improves the understanding of human typing behavior and therefore, provides significant contribution to the authentication system.

Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, [89, (2017)] DOI: 10.1016/j.patrec.2017.02.010

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