Keystroke Recognition Using WiFi Signals

Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of Channel State Information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal based keystroke recognition system called WiKey. WiKey consists of two Commercial Off-The-Shelf (COTS) WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves more than 97.5\% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%.

[1]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[2]  Pankaj Agarwal Machine Learning Toolbox , 2016 .

[3]  Kaishun Wu,et al.  FIFS: Fine-Grained Indoor Fingerprinting System , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[4]  Rakesh Agrawal,et al.  Keyboard acoustic emanations , 2004, IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004.

[5]  Meinard Müller,et al.  Information retrieval for music and motion , 2007 .

[6]  Kaishun Wu,et al.  We Can Hear You with Wi-Fi! , 2014, IEEE Transactions on Mobile Computing.

[7]  Shyamnath Gollakota,et al.  Wi-Fi Gesture Recognition on Existing Devices , 2014, ArXiv.

[8]  Bo Chen,et al.  Tracking Keystrokes Using Wireless Signals , 2015, MobiSys.

[9]  Martin Vuagnoux,et al.  Compromising Electromagnetic Emanations of Wired and Wireless Keyboards , 2009, USENIX Security Symposium.

[10]  Giovanni Vigna,et al.  ClearShot: Eavesdropping on Keyboard Input from Video , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

[11]  David Wetherall,et al.  802.11 with multiple antennas for dummies , 2010, CCRV.

[12]  Yusheng Ji,et al.  Leveraging RF-channel fluctuation for activity recognition: Active and passive systems, continuous and RSSI-based signal features , 2013, MoMM '13.

[13]  Shyamnath Gollakota,et al.  Bringing Gesture Recognition to All Devices , 2014, NSDI.

[14]  Yunhao Liu,et al.  Context-free Attacks Using Keyboard Acoustic Emanations , 2014, CCS.

[15]  Yusheng Ji,et al.  RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals , 2014, IEEE Transactions on Mobile Computing.

[16]  Kaishun Wu,et al.  WiFall: Device-free fall detection by wireless networks , 2017, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[17]  Meinard Müller,et al.  Dynamic Time Warping , 2008 .

[18]  David Wetherall,et al.  Tool release: gathering 802.11n traces with channel state information , 2011, CCRV.

[19]  Jie Yang,et al.  E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures , 2014, MobiCom.

[20]  Paul Congdon,et al.  Avoiding multipath to revive inbuilding WiFi localization , 2013, MobiSys '13.

[21]  Feng Zhou,et al.  Keyboard acoustic emanations revisited , 2005, CCS '05.

[22]  Bastien Lyonnet,et al.  Human gait classification using microDoppler time-frequency signal representations , 2010, 2010 IEEE Radar Conference.

[23]  D.P. Skinner,et al.  The cepstrum: A guide to processing , 1977, Proceedings of the IEEE.

[24]  Sneha Kumar Kasera,et al.  Temporal Link Signature Measurements for Location Distinction , 2011, IEEE Transactions on Mobile Computing.

[25]  Shaojie Tang,et al.  Electronic frog eye: Counting crowd using WiFi , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[26]  Sneha Kumar Kasera,et al.  Robust location distinction using temporal link signatures , 2007, MobiCom '07.

[27]  Rob Miller,et al.  3D Tracking via Body Radio Reflections , 2014, NSDI.

[28]  Yunhao Liu,et al.  Towards omnidirectional passive human detection , 2013, 2013 Proceedings IEEE INFOCOM.