Multi-motion sensor behavior based continuous authentication on smartphones using gated two-tower transformer fusion networks

Abstract

With continuous authentication, smartphones can constantly calculate authentication scores to verify a user's identity while interacting with the smartphones after logging in. Among various behavioral biometrics based continuous authentication methods, motion dynamics biometrics based methods draw a lot of attention, which relies on potential differences in signals from smartphone built-in motion sensors to provide an implicit and secure authentication. However, existing motion dynamics based methods not only fail to model correlations between motion sensors with multiple channels but also ignore temporal patterns contained in each channel. In this paper, we develop a two-tower neural networks based continuous authentication system TNNAuth based on the built-in multi-motion sensors on smartphones. Specifically, TNNAuth divides the raw multi-motion sensor data into channel and temporal stream data, and proposes a gated two-tower transformer fusion network to capture both time-over-channel and channel-over-time motion patterns. To evaluate TNNAuth, we collect a large-scale multi-motion sensor dataset in the unconstrained real life. Extensive experiments demonstrate that TNNAuth can provide the lowest authentication error rate, with a false-acceptance rate of 8.3% and a false-rejection rate of 9.4%.