A Dual Belief-Driven Bayesian-Stackelberg Framework for Low-Complexity and Secure Near-Field ISAC Systems

TL;DR

This paper presents a Bayesian-Stackelberg framework for near-field ISAC systems that enhances security and robustness against attacks while maintaining low computational complexity, demonstrated by significant improvements in secrecy rates and success rates.

Contribution

It introduces a dual-algorithm Bayesian-Stackelberg framework combining adaptive role switching and belief-driven resource allocation for secure, low-complexity ISAC systems.

Findings

Up to 35% increase in secrecy rates.

Success rate exceeds 98%.

Achieves robustness with minimal runtime overhead.

Abstract

Ensuring robust security in near-field Integrated Sensing and Communication (ISAC) systems remains a critical challenge due to dynamic channel conditions, multi-eavesdropper threats, and the high computational burden of real-time optimization at mmWave and THz frequencies. To address these challenges, this paper introduces a novel Bayesian-Stackelberg framework that jointly optimizes sensing, beamforming, and communication. The dual-algorithm design integrates (i) Adaptive Hybrid Node Role Switching between secure transmission and cooperative jamming (ii) Belief-Driven Sensing and Beamforming for confidence based resource allocation. The proposed unified framework significantly improves robustness against attacks while preserving linear computational complexity. Simulation results across carrier frequencies ranging from 28 to 410 GHz demonstrate that the method achieves up to a 35% increase in secrecy rates and a success rate exceeding 98%, outperforming conventional communication systems with minimal runtime overhead. These findings underscore the scalability of belief-driven ISAC security solutions for low-complexity deployment in next generation communications.