Safeguarding ISAC Performance in Low-Altitude Wireless Networks Under Channel Access Attack

TL;DR

This paper proposes a game-theoretic framework to defend low-altitude wireless networks against channel access attacks, ensuring reliable integrated sensing and communication performance for emerging air taxi applications.

Contribution

It introduces a Stackelberg game model and a backward induction algorithm to mitigate attack impacts on ISAC in LAWNs, with proven equilibrium properties.

Findings

The proposed algorithm outperforms existing baselines.

It guarantees a unique Stackelberg equilibrium.

Simulation results confirm improved ISAC performance under attack.

Abstract

The increasing saturation of terrestrial resources has driven the exploration of low-altitude applications such as air taxis. Low altitude wireless networks (LAWNs) serve as the foundation for these applications, and integrated sensing and communication (ISAC) constitutes one of the core technologies within LAWNs. However, the openness nature of low-altitude airspace makes LAWNs vulnerable to malicious channel access attacks, which degrade the ISAC performance. Therefore, this paper develops a game-based framework to mitigate the influence of the attacks on LAWNs. Concretely, we first derive expressions of communication data's signal-to-interference-plus-noise ratio and the age of information of sensing data under attack conditions, which serve as quality of service metrics. Then, we formulate the ISAC performance optimization problem as a Stackelberg game, where the attacker acts as the leader, and the legitimate drone and the ground ISAC base station act as second and first followers, respectively. On this basis, we design a backward induction algorithm that achieves the Stackelberg equilibrium while maximizing the utilities of all participants, thereby mitigating the attack-induced degradation of ISAC performance in LAWNs. We further prove the existence and uniqueness of the equilibrium. Simulation results show that the proposed algorithm outperforms existing baselines and a static Nash equilibrium benchmark, ensuring that LAWNs can provide reliable service for low-altitude applications.