AmbSentry: Mitigating Sensing Eavesdropping in ISAC Systems by Harnessing Ambient IoT Devices

2026-08-12

AI Summary

This work addresses the vulnerability of sensing information leakage in integrated sensing and communication (ISAC) systems due to the open nature of wireless channels. It proposes, for the first time, leveraging passive ambient Internet-of-Things (AIoT) devices as cooperative jammers and virtual targets to actively establish a physical-layer sensing security mechanism. By jointly optimizing base station transmit beamforming and AIoT reflection modulation, the approach injects controlled interference without relying on conventional encryption, thereby preserving sensing and communication performance for legitimate users while degrading an eavesdropper’s ability to estimate target parameters. An efficient algorithm based on the Dinkelbach transformation and block coordinate descent is developed to solve the resulting non-convex optimization problem. Experimental results demonstrate that the legitimate receiver achieves a 14 dB SNR gain in detection probability and exhibits parameter estimation errors two orders of magnitude lower than those of the eavesdropper, significantly enhancing sensing privacy and security.

Abstract

Integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for 6G networks, enabling the synergistic convergence of spectral and hardware resources to maximize system efficiency. However, the inherent openness of wireless transmission exposes ISAC systems to critical security risks, particularly regarding the privacy of the sensing information. Unauthorized sensing eavesdroppers can extract sensitive target parameters (e.g., range and velocity) by directly estimating open sensing echo channels, rendering traditional data-based protection techniques ineffective. To mitigate this threat, this paper proposes AmbSentry, an ISAC system that prevents the leakage of sensing information to sensing eavesdroppers by harnessing naturally distributed passive ambient IoT (AIoT) devices. Specifically, these AIoT devices are strategically configured to act as cooperative jammers and ghost targets, introducing controllable interference into the sensing environment. Based on the proposed system, we formulate a joint optimization problem to maximize the integrated sidelobe level at the eavesdropper under quality-of-service (QoS) constraints, thereby degrading sensing eavesdropping performance while maintaining sensing and communication performance for legitimate receivers. Since the problem is non-convex, we further develop an efficient iterative algorithm to cooperatively design the transmit beamforming at the base station and the reflection modulations of the AIoT devices based on Dinkelbach transformation and block coordinate descent methods. The detailed results also demonstrate that AmbSentry significantly enhances sensing security, allowing the legitimate sensing receiver to achieve a 14-dB SNR advantage in detection probability and a hundred times lower estimation error compared to the eavesdropper.

Problem

• ISAC
• sensing eavesdropping
• privacy
• security
• ambient IoT

Innovation

• ISAC security
• ambient IoT
• cooperative jamming
• ghost targets
• beamforming optimization