RESUMO
Compared to traditional user authentication methods, continuous user authentication (CUA) provide enhanced protection, guarantees against unauthorized access and improved user experience. However, developing effective continuous user authentication applications using the current programming languages is a daunting task mainly because of lack of abstraction methods that support continuous user authentication. Using the available language abstractions developers have to write the CUA concerns (e.g., extraction of behavioural patterns and manual checks of user authentication) from scratch resulting in unnecessary software complexity and are prone to error. In this paper, we propose new language features that support the development of applications enhanced with continuous user authentication. We develop Plascua, a continuous user authentication language extension for event detection of user bio-metrics, extracting of user patterns and modelling using machine learning and building user authentication profiles. We validate the proposed language abstractions through implementation of example case studies for CUA.