| Summary: | Day-by-day wearable devices, such as smartwatches, are getting popular with their wide range of services, including allowing financial transactions, unlocking cars, tracking health and fitness, among many others. Most often, these services are managed based on users’ personal data. Unfortunately, due to various limitations, most of the market-wearables either do not have any user authentication or have a knowledge-based authentication, such as passwords, PINs, or pattern locks, which are not only burdensome for users in the age of the internet of things (IoT) when the users are already flooded with so many passwords. Therefore, there is a need for a burden-free implicit authentication mechanism for wearables that can utilize different less-informative soft-biometric data easily obtainable from the market wearables. In this work, we present a hierarchical implicit authentication (<bold>HIAuth</bold>) system that utilizes the heart rate, gait, and breathing audio signals based on their availability to authenticate a user. From our detailed analysis, we find that binary support vector machine (SVM) classifiers with radial basis function (RBF) kernels can achieve an average accuracy of 0.98 ± 0.04 (non-sedentary) and 0.94 ± 0.03 (sedentary), <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> score of 0.98 ± 0.03 (non-sedentary) and 0.94 ± 0.04 (sedentary), and genuine rejection rate of about 0.97 ± 0.07 (non-sedentary) and 0.93 ± 0.04 (sedentary), which shows the feasibility and promise of this work.
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