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Context-Dependent Implicit Authentication for Wearable Device User

Authors :
Cheung, William
Vhaduri, Sudip
Publication Year :
2020

Abstract

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 $0.94 \pm 0.07$, $F_1$ score of $0.93 \pm 0.08$, an equal error rate (EER) of about $0.06$ at a lower confidence threshold of 0.52, which shows the promise of this work.<br />Comment: 7 pages, 5 figures, accepted at IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). arXiv admin note: substantial text overlap with arXiv:2008.10779

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2008.12145
Document Type :
Working Paper