Acoustic Side-Channel Vulnerabilities in Keyboard Input Explored Through Convolutional Neural Network Modeling: A Pilot Study

This paper presents the findings of a pilot study investigating the feasibility of recognizing keyboard keystroke sounds using Convolutional Neural Networks (CNNs) as a means of simulating an acoustic side-channel attack aimed at recovering typed text. A dedicated dataset of keyboard audio recordings was collected and preprocessed using signal-processing techniques, including Fourier-transform-based feature extraction and mel-spectrogram analysis. Data augmentation methods were applied to improve model robustness, and a CNN-based prediction architecture was developed and trained. A series of experiments was performed under multiple conditions, including controlled laboratory settings, scenarios with background noise interference, tests involving a different keyboard model, and evaluations following model quantization. The results indicate that CNN-based models can achieve high keystroke-prediction accuracy, demonstrating that this class of acoustic side-channel attacks is technically viable. Additionally, the study outlines potential mitigation strategies designed to reduce exposure to such threats. Overall, the findings highlight the need for increased awareness of acoustic side-channel vulnerabilities and underscore the importance of further research to more comprehensively understand, evaluate, and prevent attacks of this nature.