Keystroke dynamics-based user authentication using long and free text strings from various input devices

Abstract

Keystroke dynamics, which refers to the typing pattern of an individual, has been highlighted as a practical behavioral biometric feature that does not require any additional recognition device for strengthening user authentication or identification. However, research in the area of keystroke dynamics-based user authentication (KDA) has been primarily focused only on the short predefined text, such as identification (ID) and password, typed on a traditional personal computer (PC) keyboard. In this paper, we aim to explore the extendability of KDA by considering long and free text strings from various input devices. Three fundamental questions are raised about the dependence of authentication performance on (1) the type of input device, (2) the length of text strings, and (3) the type of authentication algorithm. Based on the experimental tests, we observe that (1) the usage of a PC keyboard reported the highest authentication accuracy, followed by a soft keyboard and a touch keyboard; (2) the authentication accuracy could be strengthened by increasing the length of either reference or test keystrokes; (3) the R+A and RA measures report the best performance with a PC keyboard, while the Cramér–von Mises criterion reports the best performance with the other input devices for most cases, followed by the Parzen window density estimator.