  The use of cloud services that process privacy-sensitive information such as
digital banking, pervasive healthcare, smart home applications requires an
implicit continuous authentication solution which will make these systems less
vulnerable to the spoofing attacks. Physiological signals can be used for
continuous authentication due to their personal uniqueness. Ubiquitous
wrist-worn wearable devices are equipped with photoplethysmogram sensors which
enable to extract heart rate variability (HRV) features. In this study, we show
that these devices can be used for continuous physiological authentication, for
enhancing the security of the cloud, edge services, and IoT devices. A system
that is suitable for the smartband framework comes with new challenges such as
relatively low signal quality and artifacts due to placement which were not
encountered in full lead electrocardiogram systems. After the artifact removal,
cleaned physiological signals are fed to the machine learning algorithms. In
order to train our machine learning models, we collected physiological data
using off-the-shelf smartbands and smartwatches in a real-life event.
Performance evaluation of selected machine learning algorithms shows that HRV
is a strong candidate for continuous unobtrusive implicit physiological
authentication.
