Safeguarding ISAC Performance in Low-Altitude Wireless Networks Under Channel Access Attack
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arXiv
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| Format: | Preprint |
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2025
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| author | Wang, Jiacheng He, Jialing Sun, Geng Xiong, Zehui Niyato, Dusit Mao, Shiwen Kim, Dong In Xiang, Tao |
| author_facet | Wang, Jiacheng He, Jialing Sun, Geng Xiong, Zehui Niyato, Dusit Mao, Shiwen Kim, Dong In Xiang, Tao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15838 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safeguarding ISAC Performance in Low-Altitude Wireless Networks Under Channel Access Attack Wang, Jiacheng He, Jialing Sun, Geng Xiong, Zehui Niyato, Dusit Mao, Shiwen Kim, Dong In Xiang, Tao Networking and Internet Architecture Computer Science and Game Theory Systems and Control 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. |
| title | Safeguarding ISAC Performance in Low-Altitude Wireless Networks Under Channel Access Attack |
| topic | Networking and Internet Architecture Computer Science and Game Theory Systems and Control |
| url | https://arxiv.org/abs/2508.15838 |