Continuous Typist Verification using Machine Learning

Abstract

A keyboard is a simple input device. Its function is to send keystroke information to the computer (or other device) to which it is attached. Normally this information is employed solely to produce text, but it can also be utilized as part of an authentication system. Typist verification exploits a typist’s patterns to check whether they are who they say they are, even after standard authentication schemes have confirmed their identity. This thesis investigates whether typists behave in a sufficiently unique yet consistent manner to enable an effective level of verification based on their typing patterns. Typist verification depends on more than the typist’s behaviour. The quality of the patterns and the algorithms used to compare them also determine how accurately verification is performed. This thesis sheds light on all technical aspects of the problem, including data collection, feature identification and extraction, and sample classification. A dataset has been collected that is comparable in size, timing accuracy and content to others in the field, with one important exception: it is derive

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