Keystroke identification based on Gaussian mixture models

Many computer systems rely on the username and password model to authenticate users. This method is widely used, yet it can be highly insecure if a user's login information has been compromised. To increase security, some authors have proposed keystroke patterns as a biometric tool for user authentication; they can be used to recognize users based on how they type. This paper introduces a novel method that applies GMMs to keystroke identification. The major benefit of this method is the ability to update the user's model each time he or she is authenticated. Therefore, as time goes on, each user model accurately reflects the changes in that user's keystroke pattern. Using this method, a FAR and a FRR rate of approximately 2% was achieved. However, it should be noted that 50% of the test subjects were the traditional "two finger" typists and therefore, this had a disproportionately negative impact on the results.