Data Driven Authentication: On the Effectiveness of User Behaviour Modelling with Mobile Device Sensors
This work addresses authentication security for mobile device users, but it is incremental as it builds on existing sensor-based methods with automated thresholding.
The authors tackled the problem of sensor-based authentication by proposing a lightweight user behavior modeling technique that automatically switches from training to deployment and determines detection thresholds, achieving practical insights on training duration and behavior drift across three datasets.
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.