Sensor use and usefulness: trade-offs for data-driven authentication on mobile devices

Modern mobile devices come with an array of sensors that support many interesting applications. However, sensors have different sampling costs (e.g., battery drain) and benefits (e.g., accuracy) under different circumstances. In this work we investigate the trade-off between the cost of using a sensor and the benefit gained from its use, with application to data-driven authentication on mobile devices. Current authentication practice, where user behaviour is first learned from the sensor data and then used to detect anomalies, typically assumes a fixed sampling rate and does not consider the battery consumption and usefulness of sensors. In this work we study how battery consumption and sensor effectiveness (e.g., for detecting attacks) vary when using different sensors and different sensor sampling rates. We use data from both controlled lab studies, as well as field trials, for our experiments. We also propose an adaptive sampling technique that adjusts the sampling rate based on an expected device vigilance level. Our results show that it is possible to reduce the battery consumption tenfold without significantly impacting the detection of attacks.