A New Pipeline for Snooping Keystroke Based on Deep Learning Algorithm - Massimo Orazio Spata; Valerio Maria Russo; Alessandro Ortis; Sebastiano Battiato (IEEE Access)
This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. 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, while 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 show a peak accuracy of 98.3%.