Free Text Keystroke Dynamics-based Authentication with Continuous Learning: A Case Study

Authenticating users based on their typing patterns has always been a target for cybersecurity professionals and researchers. Although previous studies have worked on free text-based authentication on mobile devices, none of them addressed the necessity of continuous learning under these settings. In this work, we propose a variational autoencoder (VAE) model that deals with this issue. As a case study, we consider a scenario where the model is initially trained on data collected from the user in a particular language (English). Then, the model is supposed to recognize the user when typing in another language (Korean). One way to adapt to that change is to retrain the model on a subset of the Korean data when it becomes available. By then, two scenarios can arise: 1) The English data still exists and the model is trained on the combination of English and Korean data; 2) The English data does not exist for security reasons or limited storage issues, and thus, we use the decoder part of our VAE to generate data based on what has been learned and then retrain the model on the mix. The average Equal Error Rate achieved among 50 participants was 3.23% and 3.55% for scenarios 1 and 2, respectively (~14% less than the baseline case where the model is not retrained). These results prove the need for continuous retraining of authentication models and highlight the efficiency of the proposed model and its ability to continuously learn, even without having access to the previous training data.