Continuous authentication with a focus on explainability

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.