We study the performance of Long Short-Term Memory networks for keystroke biometric authentication at large scale in free-text scenarios. For this we introduce TypeNet, a Recurrent Neural Network (RNN) trained with a moderate number of keystrokes per identity. We evaluate different learning approaches depending on the loss function (softmax, contrastive, and triplet loss), number of gallery samples, length of the keystroke sequences, and device type (physical vs touchscreen keyboard). With 5 gallery sequences and test sequences of length 50, 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. We utilize two Aalto University keystroke databases, one captured on physical keyboards and the second on mobile devices (touchscreen keyboards). To the best of our knowledge, both databases 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.
I. INTRODUCTIONKeystroke dynamics is a behavioral biometric trait aimed at recognizing individuals based on their typing habits. The velocity of pressing and releasing different keys [1], the hand postures during typing [2], and the pressure exerted when pressing a key [3] are some of the features taken into account by keystroke biometric algorithms aimed to discriminate among subjects. Although keystroke biometrics suffer high intra-class variability for person recognition, especially in freetext scenarios (i.e. the input text typed is not fixed between enrollment and testing), the ubiquity of keyboards as a method of text entry makes keystroke dynamics a near universal modality to authenticate subjects on the Internet.Text entry is prevalent in day-to-day applications: unlocking a smartphone, accessing a bank account, chatting with acquaintances, email composition, posting content on a social network, and e-learning [4]. As a means of subject authentication, keystroke dynamics is economical because it can be deployed on commodity hardware and remains transparent to the user. These properties have prompted several companies to capture and analyze keystrokes. The global keystroke biometrics market is projected to grow from $129.8 million dollars (2017 estimate) to $754.9 million by 2025, a rate of up to 25% per year 1 . As an example, Google has recently committed $7
