The Internet of Things (IoT) is increasingly empoweringpeople with an interconnected world of physical objectsranging from smart buildings to portable smart devices such aswearables. With recent advances in mobile sensing, wearableshave become a rich collection of portable sensors and are ableto provide various types of services including tracking of healthand fitness, making financial transactions, and unlocking smartlocks and vehicles. Most of these services are delivered based onusers’ confidential and personal data, which are stored on thesewearables. Existing explicit authentication approaches (i.e., PINsor pattern locks) for wearables suffer from several limitations,including small or no displays, risk of shoulder surfing, and users’recall burden. Oftentimes, users completely disable securityfeatures out of convenience. Therefore, there is a need for aburden-free (implicit) authentication mechanism for wearabledevice users based on easily obtainable biometric data. In thispaper, we present an implicit wearable device user authenticationmechanism using combinations of three types of coarse-grainminute-level biometrics: behavioral (step counts), physiological(heart rate), and hybrid (calorie burn and metabolic equivalentof task). From our analysis of over 400 Fitbit users from a 17-month long health study, we are able to authenticate subjectswith 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 biometricsperform better than other biometrics and behavioral biometricsdo not have a significant impact, even during non-sedentaryperiods.
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