Security Enhancement of CSI-Based Wireless Sensing via Generative AI

Integrated Sensing and Communication (ISAC) is becoming a key technology in 6 G networks, where sensing based on channel state information (CSI) plays an essential role. Present research primarily focuses on enhancing sensing performance, yet often overlooks security issue, especially the threat of unauthorized sensing that tends to receive little attention. In response to the above threat, this paper proposes to use generative AI to enhance the security of CSI-based sensing systems. Specifically, we design the guarding signal according to the characteristics of CSI fluctuations caused by user activities and build the corresponding database based on the measurements collected by software-defined radio. Utilizing the constructed dataset, we train the conditional generative diffusion model, which can produce guarding signals that are similar yet distinct from the original training samples. Then, these guarding signals are modulated onto pilot signals, effectively masking the user-induced fluctuations, thereby preventing unauthorized devices from performing illegitimate sensing. Taking the user activity recognition as the example, experimental evaluations illustrate that the proposed method reduces the recognition accuracy of unauthorized devices by about 75 %, significantly enhancing user privacy protection against unauthorized sensing.