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.

As market wearables are becoming popular with a range of services, including making financial transactions, accessing cars, etc. that they provide based on various private information of a user, security of this information is becoming very important. However, users are often flooded with PINs and passwords in this internet of things (IoT) world. Additionally, hard-biometric, such as facial or finger recognition, based authentications are not adaptable for market wearables due to their limited sensing and computation capabilities. Therefore, it is a time demand to develop a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from the market wearables. In this work, we present a context-dependent soft-biometric-based wearable authentication system utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that a binary support vector machine (SVM) with radial basis function (RBF) kernel can achieve an average accuracy of 0.94 ± 0.07, F1score of 0.93 ± 0.08, an equal error rate (EER) of about 0.06 at a lower confidence threshold of 0.52, which shows the promise of this work.