Visible to the public Exploring Key-level Analytics for Computational Modeling of Typing Behavior

TitleExploring Key-level Analytics for Computational Modeling of Typing Behavior
Publication TypeConference Paper
Year of Publication2014
AuthorsChakraborty, Arpan, Harrison, Brent, Yang, Pu, Roberts, David, St. Amant, Robert
Conference NameProceedings of the 2014 Symposium and Bootcamp on the Science of Security
Conference LocationRaleigh, NC, USA
ISBN Number978-1-4503-2907-1
Keywordscognitive modeling, Foundations, Human and Societal Aspects of Security and Privacy, science of security, Social Aspects of Security and Privacy, typing, user interfaces

Typing is a human activity that can be affected by a number of situational and task-specific factors. Changes in typing behavior resulting from the manipulation of such factors can be predictably observed through key-level input analytics. Here we present a study designed to explore these relationships. Participants play a typing game in which letter composition, word length and number of words appearing together are varied across levels. Inter-keystroke timings and other higher order statistics (such as bursts and pauses), as well as typing strategies, are analyzed from game logs to find the best set of metrics that quantify the effect that different experimental factors have on observable metrics. Beyond task-specific factors, we also study the effects of habituation by recording changes in performance with practice. Currently a work in progress, this research aims at developing a predictive model of human typing. We believe this insight can lead to the development of novel security proofs for interactive systems that can be deployed on existing infrastructure with minimal overhead. Possible applications of such predictive capabilities include anomalous behavior detection, authentication using typing signatures, bot detection using word challenges etc.

Citation KeyChakraborty:2014:EKA:2600176.2600210