Bag of On-Phone ANNs to Secure IoT Objects Using Wearable and Smartphone Biometrics
Sudip Vhaduri, William Cheung, Sayanton Vhaduri Dibbo · IEEE Transactions on Dependable and Secure Computing · 2023
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$.973 \pm . 004$. Next, we implement the bag of models on smartphones using Google's TensorFlow Lite framework-supportedTFL Authapplication, which requires around 56-65 KB memory and can verify a user in 5 seconds. Finally, we evaluate the systemTFL Authusing 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.