  Keystroke dynamics study the way in which users input text via their
keyboards. Having the ability to differentiate users, typing behaviors can
unobtrusively form a component of a behavioral biometric recognition system to
improve existing account security. Keystroke dynamics systems on free-text data
have previously required 500 or more characters to achieve reasonable
performance. In this paper, we propose a novel instance-based graph comparison
algorithm called the instance-based tail area density (ITAD) metric to reduce
the number of keystrokes required to authenticate users. Additionally, commonly
used features in the keystroke dynamics literature, such as monographs and
digraphs, are all found to be useful in informing who is typing. The usefulness
of these features for authentication is determined using a random forest
classifier and validated across two publicly available datasets. Scores from
the individual features are fused to form a single matching score. With the
fused matching score and our ITAD metric, we achieve equal error rates (EERs)
for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset,
improving upon state-of-the-art of 35.3% and 15.3%.
