Publication Type

Conference Proceeding Article

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org./10.1007/978-3-319-60033-8_59

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