Conference Proceeding Article
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.
Wearable devices, User authentication, Sensor, Machine learning
Information Security | Software Engineering
Wireless Algorithms, Systems, and Applications: WASA 2017: Guilin, China, June 19-21: Proceedings
City or Country
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3804
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