Keystroke dynamics as a biometric
University of Southampton, School of Electronics and Computer Science,
Modern computer systems rely heavily on methods of authentication and identity verification to protect sensitive data. One of the most robust protective techniques involves adding a layer of biometric analysis to other security mechanisms, as a means of establishing the identity of an individual beyond reasonable doubt. In the search for a biometric technique which is both low-cost and transparent to the end user, researchers
have considered analysing the typing patterns of keyboard users to determine their characteristic timing signatures.
Previous research into keystroke analysis has either required fixed performance of known keyboard input or relied on artificial tests involving the improvisation of a block of text for analysis. I is proposed that this is insufficient to determine the nature of
unconstrained typing in a live computing environment. In an attempt to assess the utility of typing analysis for improving intrusion detection on computer systems, we present the notion of ‘genuinely free text’ (GFT). Through the course of this thesis, we discuss the nature of GFT and attempt to address whether it is feasible to produce a lightweight software platform for monitoring GFT keystroke biometrics, while protecting
the privacy of users.
The thesis documents in depth the design, development and deployment of the multigraph-based BAKER software platform, a system for collecting statistical GFT data from live environments. This software platform has enabled the collection of an extensive set of keystroke biometric data for a group of participating computer users, the analysis of which we also present here. Several supervised learning techniques were
used to demonstrate that the richness of keystroke information gathered from BAKER is indeed sufficient to recommend multigraph keystroke analysis, as a means of augmenting
computer security. In addition, we present a discussion of the feasibility of applying data obtained from GFT profiles in circumventing traditional static and free text analysis biometrics.
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