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.
CITATION STYLE
Chang, B., Liu, X., Li, Y., Wang, P., Zhu, W. T., & Wang, Z. (2017). Employing smartwatch for enhanced password authentication. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10251 LNCS, pp. 691–703). Springer Verlag. https://doi.org/10.1007/978-3-319-60033-8_59
Mendeley helps you to discover research relevant for your work.