Keystroke privacy is crucial for securing smartphone systems 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.

按键隐私对于保护智能手机系统和用户隐私至关重要。如果对手以某种方式获得了数字软键盘上的击键序列，则用户的私人信息可能会泄漏。不同的击键可能导致不同的手指移动，从而导致对WiFi信号的各种干扰，这可以通过信道状态信息（CSI）波形的波动来表示。在本文中，我们提出了WiPass，这是一种用于对智能手机上的数字键盘输入进行分类的击键识别系统，它由发射器（例如，WiFi路由器）和接收器（例如，具有商用现货（COTS）的台式计算机）组成WiFi NIC）。关键灵感来自以下事实：在接收器附近执行特定的击键操作时，接收器接收到的CSI值会以唯一的模式变化。WiPass可以提取和分析由用户在智能手机上的按键操作生成的CSI数据，从而通过比较和分类不同按键的CSI波形来推断用户的数字按键。与以前的按键推理方法不同，WiPass采用1D卷积神经网络（1D-CNN）模型作为分类模型，而不是其他机器学习模型。实验结果表明，WiPass在检测击键和击键分类中的准确率分别达到95％和85％以上。WiPass采用1D卷积神经网络（1D-CNN）模型作为分类模型，而不是其他机器学习模型。实验结果表明，WiPass在检测击键和击键分类中的准确率分别达到95％和85％以上。WiPass采用1D卷积神经网络（1D-CNN）模型作为分类模型，而不是其他机器学习模型。实验结果表明，WiPass在检测击键和击键分类中的准确率分别达到95％和85％以上。