Generative AI based Secure Wireless Sensing for ISAC Networks
Jiacheng Wang, Hongyang Du, Yinqiu Liu, Geng Sun, Dusit Niyato, Shiwen, Mao, Dong In Kim, and Xuemin Shen

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…
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Taxonomy
TopicsSecurity in Wireless Sensor Networks · Energy Efficient Wireless Sensor Networks · Network Security and Intrusion Detection
MethodsDiffusion
