  We propose a lightweight, and temporally and spatially aware user behaviour
modelling technique for sensor-based authentication. Operating in the
background, our data driven technique compares current behaviour with a user
profile. If the behaviour deviates sufficiently from the established norm,
actions such as explicit authentication can be triggered. To support a quick
and lightweight deployment, our solution automatically switches from training
mode to deployment mode when the user's behaviour is sufficiently learned.
Furthermore, it allows the device to automatically determine a suitable
detection threshold. We use our model to investigate practical aspects of
sensor-based authentication by applying it to three publicly available data
sets, computing expected times for training duration and behaviour drift. We
also test our model with scenarios involving an attacker with varying knowledge
and capabilities.
