Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2017
Abstract
This paper presents an enhanced password authentication scheme by systematically exploiting the motion sensors in a smartwatch. We extract unique features from the sensor data when a smartwatch bearer types his/her password (or PIN), and train certain machine learning classifiers using these features. We then implement smartwatch-aided password authentication using the classifiers. Our scheme is user-friendly since it does not require users to perform any additional actions when typing passwords or PINs other than wearing smartwatches. We conduct a user study involving 51 participants on the developed prototype so as to evaluate its feasibility and performance. Experimental results show that the best classifier for our system is the Bagged Decision Trees, for which the accuracy is 4.58% FRR and 0.12% FAR on the QWERTY keyboard, and 6.13% FRR and 0.16% FAR on the numeric keypad.
Keywords
Wearable devices, User authentication, Sensor, Machine learning
Discipline
Information Security | Software Engineering
Research Areas
Cybersecurity
Publication
Wireless Algorithms, Systems, and Applications: WASA 2017: Guilin, China, June 19-21: Proceedings
Volume
10251
First Page
691
Last Page
703
ISBN
9783319600338
Identifier
10.1007/978-3-319-60033-8_59
Publisher
Springer Verlag
City or Country
Cham
Citation
CHANG, Bing; LIU, Ximing; LI, Yingjiu; WANG, Pingjian; ZHU, Wen-Tao; and WANG, Zhan.
Employing smartwatch for enhanced password authentication. (2017). Wireless Algorithms, Systems, and Applications: WASA 2017: Guilin, China, June 19-21: Proceedings. 10251, 691-703.
Available at: https://ink.library.smu.edu.sg/sis_research/3804
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-319-60033-8_59