Adversarial Attack and Defense for WiFi-based Apnea Detection System
Conference
Ambalkar, H, Zhao, T, Wang, X et al. (2023). Adversarial Attack and Defense for WiFi-based Apnea Detection System
. 10.1109/INFOCOMWKSHPS57453.2023.10225824
Ambalkar, H, Zhao, T, Wang, X et al. (2023). Adversarial Attack and Defense for WiFi-based Apnea Detection System
. 10.1109/INFOCOMWKSHPS57453.2023.10225824
WiFi sensing systems have gained enormous interest in extensive areas, including vital sign monitoring. By using deep neural networks (DNNs), WiFi sensing systems can achieve high performance. However, the security and vulnerability of DNNs under adversarial attack would greatly affect the WiFi sensing performance. In this paper, we develop a DNN-based apnea detection system using WiFi channel state information (CSI) and evaluate its robustness under three different attacks. The experimental results show that adversarial attacks can significantly impact the model performance, and the defense scheme (i.e. adversarial training) can improve the system robustness.
