Bag of On-Phone ANNs to Secure IoT Objects Using Wearable and Smartphone Biometrics

Abstract

The introduction of the Internet of Things (IoT) has made several emerging applications, from financial transactions to property access, possible through IoT-connected smart wearables (smartwatches). This creates an immediate need for an authentication system that can validate a user seamlessly, compared to knowledge-based approaches. In this work, we present an implicit authentication system that utilizes a bag of on-phone artificial neural network (ANN) models to validate a user based on the availability of three soft-biometrics (heart rate, gait, and breathing patterns) collected from smartphones and Fitbits. We find that using all three biometrics we can achieve an average accuracy of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$.973 \pm . 004$</tex-math></inline-formula> . Next, we implement the bag of models on smartphones using Google's TensorFlow Lite framework-supported <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TFL Auth</i> application, which requires around 56-65 KB memory and can verify a user in 5 seconds. Finally, we evaluate the system <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TFL Auth</i> using two cohorts of 25 subjects in total, and we find that the system has average understandability and importance scores of around 4.0 and 4.3 on a 1 – 5 scale.