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The Internet of Things (IoT) is increasingly empowering people with aninterconnected world of physical objects ranging from smart buildings toportable smart devices such as wearables. With recent advances in mobilesensing, wearables have become a rich collection of portable sensors and areable to provide various types of services including tracking of health andfitness, making financial transactions, and unlocking smart locks and vehicles.Most of these services are delivered based on users' confidential and personaldata, which are stored on these wearables. Existing explicit authenticationapproaches (i.e., PINs or pattern locks) for wearables suffer from severallimitations, including small or no displays, risk of shoulder surfing, andusers' recall burden. Oftentimes, users completely disable security featuresout of convenience. Therefore, there is a need for a burden-free (implicit)authentication mechanism for wearable device users based on easily obtainablebiometric data. In this paper, we present an implicit wearable device userauthentication mechanism using combinations of three types of coarse-grainminute-level biometrics: behavioral (step counts), physiological (heart rate),and hybrid (calorie burn and metabolic equivalent of task). From our analysisof over 400 Fitbit users from a 17-month long health study, we are able toauthenticate subjects with average accuracy values of around .93 (sedentary)and .90 (non-sedentary) with equal error rates of .05 using binary SVMclassifiers. Our findings also show that the hybrid biometrics perform betterthan other biometrics and behavioral biometrics do not have a significantimpact, even during non-sedentary periods.

Summary: Multi-modal Biometric-based Implicit Authentication of Wearable Device Users