A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm

This research focuses on the vulnerability issues related to keystroke logging on a physical computer keyboard, known as Snooping Keystrokes. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, and key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results for the proposed pipeline show a peak accuracy of 98.3%.