Normalizing flow-based latent space mapping for implicit pattern authentication on mobile devices

In numerous mobile device applications, safeguarding user privacy through authentication is of paramount importance. Traditional methods employ information-matching-based systems, wherein users authenticate themselves using preset passwords. However, such systems pose a challenge in maintaining privacy if the password is compromised. This study introduces a behavior-based authentication approach that capitalizes on implicit patterns generated by individual users’ device usage habits during password entry on mobile devices, complementing information-based authentication. To optimize this feature, we develop a user-specific latent space mapping framework utilizing a normalizing flow and train a one-class classification machine learning model based on the latent space to enhance authentication performance. Upon applying the proposed methodology to data from 1,000 mobile banking service users, we observed a significant improvement in authentication performance. The integrated error decreased from 21.42% to 9.48%, and the equal error rate reduced from 27.84% to 16.3% compared to the method solely employing a machine learning model in the data space without implementing latent space mapping.