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

Data Driven Authentication: On the Effectiveness of User Behaviour Modelling with Mobile Device Sensors