Continuous authentication with a focus on explainability

Abstract

Traditional explicit authentication mechanisms, in which the device remains unlocked after the introduction of some kind of password, are slowly being complemented with the so-called implicit or continuous authentication mechanisms. In the latter, the user is constantly monitored in one or more ways, in search for signs of unauthorized access, which may happen if a third party has access to the phone after it has been unlocked. There are some different forms of continuous authentication, some of which based on Machine Learning. These are generally black box models, that provide a decision but not an explanation. In this paper we propose an approach for continuous authentication based on behavioral biometrics, machine learning, and that includes domain-dependent aspects for the user to interpret the actions and decisions of the system. It is non-intrusive, does not require any additional hardware, and can be used continuously to monitor user identity.