WiPass: 1D-CNN-based smartphone keystroke recognition Using WiFi signals

Abstract

Keystroke privacy is crucial for smartphone system security and user privacy. User’s private information may be leaked if the sequences of keystrokes on the numeric soft keyboard are obtained by an adversary in a certain way. Different keystrokes may lead to different finger movements, causing diverse interference to WiFi signals, which can be indicated by the fluctuation of Channel State Information (CSI) waveforms. In this paper, we propose WiPass, a keystroke recognition system for classifying numeric keyboard inputs on smartphones, which consists of a transmitter (e.g. a WiFi router) and a receiver (e.g. a desktop computer with a Commercial Off-The-Shelf (COTS) WiFi NIC). The key inspiration comes from the fact that while performing a certain keystroke near a receiver, the CSI values received by the receiver vary in a unique pattern. WiPass can extract and analyze the CSI Data generated by user’s keystroke operations on the smartphone, thus inferring the users’ numeric keystrokes by comparing and classifying the CSI waveforms of the different keystrokes. Distinct from the previous keystroke inference approaches, WiPass employs 1D Convolutional Neural Network (1D-CNN) model as the classification model instead of other machine learning models. The experimental results show that the accuracy rate of the WiPass in detecting a keystroke and in classifying keystrokes reaches over 95% and 85% respectively.