Smartwatch User Authentication by Sensing Tapping Rhythms and Using One-Class DBSCAN

Sensors

Wei Zhou¹  Changsheng Dou²  Xi Xiao³  Shiguang Ni³  Hanqi Zhang³  Shutao Xia³ 

[1] School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3000, Australia;School of Statistics, Capital University of Economics and Business, Beijing 100000, China;Tsinghua Shenzhen International Graduate School, Shenzhen 518000, China;

关键词: one-class classification;    DBSCAN;    smartwatch;    tapping rhythm;    sensor;    authentication;   

DOI  :  10.3390/s21072456

来源: DOAJ

【 摘 要 】

As important sensors in smart sensing systems, smartwatches are becoming more and more popular. Authentication can help protect the security and privacy of users. In addition to the classic authentication methods, behavioral factors can be used as robust measures for this purpose. This study proposes a lightweight authentication method for smartwatches based on edge computing, which identifies users by their tapping rhythms. Based on the DBSCAN clustering algorithm, a new classification method called One-Class DBSCAN is presented. It first seeks core objects and then leverages them to perform user authentication. We conducted extensive experiments on 6110 real data samples collected from more than 600 users. The results show that our method achieved the lowest Equal Error Rate ($EER$) of only 0.92%, which was lower than those of other state-of-the-art methods. In addition, a statistical method for detecting the security level of a tapping rhythm is proposed. It can prevent users from setting a simple tapping rhythm password, and thus improve the security of smartwatches.

【 授权许可】

Unknown