  We study the performance of Long Short-Term Memory networks for keystroke
biometric authentication at large scale in free-text scenarios. For this we
explore the performance of Long Short-Term Memory (LSTMs) networks trained with
a moderate number of keystrokes per identity and evaluated under different
scenarios including: i) three learning approaches depending on the loss
function (softmax, contrastive, and triplet loss); ii) different number of
training samples and lengths of keystroke sequences; iii) four databases based
on two device types (physical vs touchscreen keyboard); and iv) comparison with
existing approaches based on both traditional statistical methods and deep
learning architectures. Our approach called TypeNet achieves state-of-the-art
keystroke biometric authentication performance with an Equal Error Rate of 2.2%
and 9.2% for physical and touchscreen keyboards, respectively, significantly
outperforming previous approaches. Our experiments demonstrate a moderate
increase in error with up to 100,000 subjects, demonstrating the potential of
TypeNet to operate at an Internet scale. To the best of our knowledge, the
databases used in this work are the largest existing free-text keystroke
databases available for research with more than 136 million keystrokes from
168,000 subjects in physical keyboards, and 60,000 subjects with more than 63
million keystrokes acquired on mobile touchscreens.
