Abstract
Sensor data on a user’s mobile device can often be used to identify the user for improving the security of smartphones in indoor environments. In this paper, we present a novel continuous user identification system called LightLock that collects light sensor data from a user’s smartphone and analyzes them to identify a specific user using a machine learning approach. We develop a multi-model system to extract four different feature vectors: (1) absolute time series (ATS); (2) auto-correlation function (ACF); (3) level crossing rate (LCR); and (4) peak readings detection (PRD). To show the feasibility of LightLock, we implemented an Android application and evaluated the performance of LightLock on the dataset collected during a period of 20 days. LightLock achieves over 98% accuracy in identifying a specific user. LightLock also provides an accurate and cost-less alternative solution to existing approaches that require explicit user-smartphone interaction or the high energy consumption of multiple sensors.
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