A Siamese Neural Network for Behavioral Biometrics Authentication
The raise in popularity of personalized web and mobile applications brings about a need of robust authentication systems. Although password authentication is the most popular authentication mechanism, it has also several drawbacks. Behavioral Biometrics Authentication has emerged as a complementary risk-based authentication approach which aims at profiling users based on their behavior while interacting with computers/smartphones. In this work we propose a novel Siamese Neural Network to perform a few-shot verification of user's behavior. We develop our approach to identify behavior from 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. We show that our approach has 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 different web interactions collected in the wild.
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