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

TL;DR

This paper introduces a lightweight, adaptive user behavior modeling technique using mobile sensors for authentication, capable of automatic training, threshold setting, and effective detection of deviations for enhanced security.

Contribution

It presents a novel, automatic, and adaptive sensor-based authentication method that operates efficiently in real-world scenarios with minimal user intervention.

Findings

Model effectively detects behavior deviations for authentication

Automatic training and threshold setting improve deployment ease

Scenario testing shows robustness against various attacker capabilities

Abstract

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.