A Siamese Neural Network for Scalable Behavioral Biometrics Authentication

The rise in popularity of web and mobile applications brings about a need of robust authentication systems. Behavioral Biometrics Authentication has emerged as a complementary risk-based authentication approach which aims at profiling users based on their interaction with computers/smartphones. In this work we propose a novel approach based on Siamese Neural Networks to perform a few-shot verification of user's behavior. We develop our approach to authenticate either human-computer or human-smartphone interaction. For computer interaction, our approach learns from mouse and keyboard dynamics, while for smartphone interaction it learns from holding patterns and touch patterns. The proposed approach requires only one model to authenticate all the users of a system, as opposed to the one model per user paradigm. This is a key aspect with respect to the scalability of our approach. The proposed model exhibits a few-shot classification accuracy of up to 99.8% and 90.8% for mobile and web interactions, respectively. We also test our approach on a database that contains over 100K interactions collected in the wild.