Abstract
The security of wearable devices user’s privacy data has become more and more concerned because of the high accuracy of the embedded sensors. Existing methods of obtaining privacy data often rely on installations of dedicated hardware, or accurate numerical calculation of sensor data, which do not have flexible adaptability. In this paper we utilize a multi-SVM and a KNN classifier using only accelerometer data and fuzzy coordinates to get the privacy data such as password directly with a higher accuracy.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 61403301 and Grant 61773310, in part by the China Postdoctoral Science Foundation under Grant 2014M560783 and Grant 2015T81032, in part by the Natural Science Foundation of Shaanxi Province under Grant 2015JQ6216, and in part by the Fundamental Research Funds for the Central Universities under Grant xjj2015115.
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Shen, C., Ren, Z., Chen, Y., Wang, Z. (2017). On Using Wearable Devices to Steal Your Passwords: A Fuzzy Inference Approach. In: Wen, S., Wu, W., Castiglione, A. (eds) Cyberspace Safety and Security. CSS 2017. Lecture Notes in Computer Science(), vol 10581. Springer, Cham. https://doi.org/10.1007/978-3-319-69471-9_38
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DOI: https://doi.org/10.1007/978-3-319-69471-9_38
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