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September 4, 2016

Keystroke Recognition Using WiFi Signals [Identifying Users With WiFi?]

Filed under: Cybersecurity,Security — Patrick Durusau @ 8:42 pm

Keystroke Recognition Using WiFi Signals by Kamran Ali, Alex X. Liu, Wei Wang, and Muhammad Shahzad.

Abstract:

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%.

In discussing the limitations of their technique the authors mention:


User Specific Training. In our current implementation of WiKey, we train the classifiers using one user and test the classifier using the test samples from the same user. However, we hypothesize that if we train our classifier using a large number of users, the trained classifier will be able to capture commonalities between users and will then be able to recognize the keystrokes of any unknown user. At the same time, we also acknowledge that it is extremely challenging to build such a universal classifier that works for almost every user because WiFi signals are susceptible to various factors such as finger length/width, typing styles, and environmental noise.

The more interesting case would be identifying users in surveillance mode by their keystrokes, assuming persistent digital capture of their keystrokes wasn’t possible.

Subject (as in human) identification by WiFi signals?

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