Abstract
The increasing credit card consumption makes the security of keypad input become a problem that cannot be ignored. We propose a novel keystroke recognition system called WiKey. When the user enters the password on the keypad with his/her fingers, the posture and position of different keystrokes will introduce a unique interference to the multi-path signals, which can be reflected by the Channel State Information. After analysis of the fluctuation of the CSI waveform between two keystrokes, we find that there is a strong correlation between the distance of finger movement and the shape of the waveform. We exploit the association to infer the user’s number input. Compared with the previous approaches of keystroke inference, the use of auxiliary information improves their cognition accuracy. We implemented the WiKey in the normal Point Of Sale. The results of experiment show that the average accuracy rate is about 90%, which are 5–10% higher than the rate of the previous keystroke inference approaches.
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Acknowledgements
The authors would like to thank the reviewers for their insight and comments on this paper. This work was supported by the National Natural Science Foundation of China (Grant No. 61272422 and 61202353).
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Yunfang, C., Yihong, Z., Hao, Z., Wei, C., Wei, Z. (2018). Enhanced Keystroke Recognition Based on Moving Distance of Keystrokes Through WiFi. In: Au, M., et al. Network and System Security. NSS 2018. Lecture Notes in Computer Science(), vol 11058. Springer, Cham. https://doi.org/10.1007/978-3-030-02744-5_18
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DOI: https://doi.org/10.1007/978-3-030-02744-5_18
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