  The authentication field is evolving towards mechanisms able to keep users
continuously authenticated without the necessity of remembering or possessing
authentication credentials. While existing continuous authentication systems
have demonstrated their suitability for single-device scenarios, the Internet
of Things and next generation of mobile networks (5G) are enabling novel
multi-device scenarios -- such as Smart Offices -- where continuous
authentication is still an open challenge. The paper at hand, proposes an
AI-based, privacy-preserving and multi-device continuous authentication
architecture called AuthCODE. A realistic Smart Office scenario with several
users, interacting with their mobile devices and personal computer, has been
used to create a set of single- and multi-device behavioural datasets and
validate AuthCODE. A pool of experiments with machine and deep learning
classifiers measured the impact of time in authentication accuracy and improved
the results of single-device approaches by considering multi-device behaviour
profiles. The f1-score average reached for XGBoost on multi-device profiles
based on 1-minute windows was 99.33%, while the best performance achieved for
single devices was lower than 97.39%. The inclusion of temporal information in
the form of vector sequences classified by a Long-Short Term Memory Network,
allowed the identification of additional complex behaviour patterns associated
to each user, resulting in an average f1-score of 99.02% on identification of
long-term behaviours.
