Behavioral Biometrics-Based Continuous Authentication Using a Lightweight Latent Representation Masked One-Class Autoencoder

Behavioral biometrics-based continuous authentication has proven to be an excellent supplement to one-time authentication schemes that applies behavioral biometrics to authenticate smartphone users’ identities throughout the session. However, it still has two key issues that need to be addressed: 1) Due to behavioral biometrics from attackers are not available a priori, continuous authentication models should be trained only with normal samples in an unsupervised manner rather than treated as binary or multi-class classification tasks; 2) Differences in behavioral biometrics between attackers and legitimate users are fine-grained, it is challenging to extract rich semantic information to model users’ behavioral patterns. To fill this gap, we propose a lightweight latent representation masked one-class autoencoder, which is trained only with legitimate users’ behavioral biometrics. It consists of two parts: masked latent representation generator (MLRG) and Reconstructor. First, we apply the MLRG to generate discriminative latent representation with low dimension and then as a mask to cover important parts of the latent representation generated from the Reconstructor. Second, we apply the Reconstructor to reconstruct input based on masked latent representation. Experimental results demonstrate that our approach achieves superior authentication performance of 0.68% EER, 0.94% EER, 2.14% EER, and 1.87% EER on four datasets, respectively.