Data Mining a Keystroke Dynamics Based Biometrics Database Using Rough Sets

Software based biometrics, utilising keystroke dynamics has been proposed as a cost effective means of enhancing computer access security. Keystroke dynamics has been successfully employed as a means of identifying legitimate/illegitimate login attempts based on the typing style of the login entry. In this paper, we collected keystroke dynamics data in the form of digraphs from a series of users entering a specific login ID. We wished to determine if there were any particular patterns in the typing styles that would indicate whether a login attempt was legitimate or not using rough sets. Our analysis produced a sensitivity of 96%, specificity of 93% and an overall accuracy of 95%. The results of this study indicate that typing speed and the first few and the last few characters of the login ID were the most important indicators of whether the login attempt was legitimate or not