2015 · 536 citations · 28 references
Wifi SignalWifi DevicesEngineeringWireless LanBiometricsWifi SignalsWireless ComputingSpeech RecognitionHardware SecurityWireless SecurityPattern RecognitionSecure CommunicationInput DeviceMobile ComputingComputer ScienceCovert ChannelStatistical Pattern RecognitionSignal ProcessingCryptographySpeech ProcessingPattern Recognition Application
Keystroke privacy is critical because typed content may include passwords or sensitive information. The paper proposes WiKey, a WiFi‑based keystroke recognition system. WiKey uses two commercial off‑the‑shelf WiFi devices—a router and a laptop—to continuously transmit and receive signals, extracting unique CSI‑waveform patterns generated by hand movements during typing to identify keys. WiKey achieves over 97.5 % detection rate and 96.4 % single‑key accuracy, and 93.5 % accuracy for continuous sentence typing in real‑world experiments.
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%.
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