Generative AI based Secure Wireless Sensing for ISAC Networks

TL;DR

This paper introduces a novel secure sensing system for ISAC networks using diffusion models to generate safeguarding signals, significantly reducing unauthorized device sensing accuracy and enhancing security in 6G wireless systems.

Contribution

The paper proposes a new secure sensing framework (DFSS) utilizing diffusion models to protect CSI-based sensing in ISAC networks from unauthorized access.

Findings

Reduces unauthorized sensing accuracy by approximately 70%.

Effectively shields users from unauthorized surveillance.

Demonstrates the feasibility of diffusion models for security in wireless sensing.

Abstract

Integrated sensing and communications (ISAC) is expected to be a key technology for 6G, and channel state information (CSI) based sensing is a key component of ISAC. However, current research on ISAC focuses mainly on improving sensing performance, overlooking security issues, particularly the unauthorized sensing of users. In this paper, we propose a secure sensing system (DFSS) based on two distinct diffusion models. Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, guiding the ISAC system to appropriately activate wireless links and nodes, which ensures the sensing performance while minimizing the operation cost. Using the activated links and nodes, DFSS then employs the continuous conditional diffusion model to generate safeguarding signals, which are next modulated onto the pilot at the transmitter to mask fluctuations caused by user activities. As such, only ISAC devices authorized with the safeguarding signals can extract the true CSI for sensing, while unauthorized devices are unable to achieve the same sensing. Experiment results demonstrate that DFSS can reduce the activity recognition accuracy of the unauthorized devices by approximately 70%, effectively shield the user from the unauthorized surveillance.