Acoustic Side Channel Attack for Keystroke Splitting in the Wild

With the increasing prevalence of smart devices in our daily lives, concerns about security vulnerabilities have become more prominent. Acoustic Side Channel Attacks (ASCA) have emerged as a significant threat, exploiting sound emissions from devices to infer sensitive information such as keystrokes or conversations. We propose a novel approach leveraging deep learning techniques for audio segmentation in ASCA scenarios. Our method involves preprocessing the audio data, extracting relevant features, and applying a Temporal Convolutional Network (TCN) model for keystroke classification. We conducted experiments on a diverse dataset comprising various types of smart devices and attack scenarios. Our experiments demonstrate the effectiveness of the proposed audio segmentation method as fundamental preparatory step in ASCA attacks. The segmentation model achieved high precision, sensitivity, and specificity values, indicating its robustness in accurately. Furthermore, we observed consistent performance across different types of smart devices and attack scenarios, highlighting the generalizability of our approach in real conditions. The high precision, sensitivity, and specificity values obtained in our evaluation underscore the reliability and practical utility of the proposed approach. Experimental results demonstrate a peak accuracy of 990%, showcasing the effectiveness and precision of the approach. This high level of accuracy underscores the reliability and potential real-world applicability of the findings for keystroke splitting in the wild.