Enhanced Keystroke Recognition Based on Moving Distance of Keystrokes Through WiFi
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.
KeywordsChannel state information Wireless security Keystroke recognition Auxiliary information
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).
- 2.Wang, Y., Liu, J., Chen, Y., Gruteser, M., Yang, J., Liu, H.: E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures. In: MobiCom 2014, September 7–11, 2014, Maui, Hawaii, USA, pp. 617–628. ACM (2014)Google Scholar
- 3.Zeng, Y., Pathak, P.H., Xu, C., Mohapatra, P.: Your AP knows how you move: fine-grained device motion recognition through WiFi. In: HotWireless 2014, September 11, 2014, Maui, Hawaii, USA (2014)Google Scholar
- 4.Wang, W., Liu, A.X., Shahzad, M., et al.: Understanding and modeling of WiFi signal based human activity recognition. In: International Conference on Mobile Computing and NETWORKING, pp. 65–76. ACM (2015)Google Scholar
- 6.Pu, Q., Gupta, S., Gollakota, S., et al.: Whole-home gesture recognition using wireless signals. In: International Conference on Mobile Computing & Networking, pp. 27–38. ACM (2013)Google Scholar
- 7.Wang, W., Liu, A.X., Shahzad, M., Ling, K., Lu, S.: Understanding and modeling of WiFi signal based human activity recognition. In: MobiCom 2015, September 7–11, 2015, Paris, France, pp. 65–76. ACM (2015)Google Scholar
- 8.Abdelnasser, H., Harras, K.A., Youssef, M.: WiGest demo: a ubiquitous WiFi-based gesture recognition system. In: IEEE Infocom 2015 Live/Video Demonstration, pp. 17–18 (2015)Google Scholar
- 9.Wang, G., Zou, Y., Zhou, Z., Wu, K., Ni, L.M.: We can hear you with Wi-Fi! In: MobiCom 2014, September 7–11, 2014, Maui, Hawaii, USA, pp. 593–604 (2014)Google Scholar
- 10.Li, M., Meng, Y., Liu, J., et al.: When CSI meets public WiFi: inferring your mobile phone password via WiFi signals. In: ACM SIGSAC Conference, pp. 1068–1079. ACM (2016)Google Scholar
- 11.Ali, K., Liu, A.X., Wang, W., et al.: Keystroke recognition using WiFi signals. In: International Conference on Mobile Computing and Networking, pp. 90–102. ACM (2015)Google Scholar
- 15.Shlens, J.: A tutorial on principal component analysis. 51(3), 219–226 (2014)Google Scholar