Improved authentication mechanisms are needed to cope with the increased data exposure we face nowadays. Keystroke dynamics is a cost-effective alternative, which usually only requires a standard keyboard to acquire authentication data. Here, we focus on recognizing users by keystroke dynamics using immune algorithms, considering a one-class classification approach. In such a scenario, only samples from the legitimate user are available to generate the model of the user. Throughout the paper, we emphasize the importance of proper data understanding and pre-processing. We show that keystroke samples from the same user present similarities in what we call typing signature. A proposal to take advantage of this finding is discussed: the use of rank transformation. This transformation improved performance of classification algorithms tested here and it was decisive for some immune algorithms studied in our setting. 
Emphasizing typing signature in keystroke dynamics using immune algorithms