Fixed-text keystroke dynamics authentication data set—collection and analysis

Abstract

AbstractKeystroke dynamics authentication is a method of authenticating a user and could be an alternative or addition to one-time codes, with minimal user inconvenience. In this study, a new data set was collected for 6 unique passwords, adding to the limited available data sets for keystroke dynamics available for researchers. Data was collected by emulating legitimate users familiar with the passwords and a wider range of attackers with limited login attempts. The data set is analyzed with the use of various methods, and the effects of password length and complexity are investigated. Two algorithms were employed, one achieving an average equal error rate varying between 10.2 and 18.1% depending on the password, and the other method achieving an average true accept rate of 98% and true reject rate of 90.4% by comparing across multiple individuals in the data set. These results provide a benchmark for further studies on this data set.