Stackelberg Game-Driven Defense for ISAC Against Channel Attacks in Low-Altitude Networks

TL;DR

The paper tackles ISAC performance security in low-altitude wireless networks under channel access attacks by formulating a three-player Stackelberg game with an attacker as the leader and a RIS assisted drone plus an ISAC base station as followers. It introduces an AoI based metric for sensing freshness and develops a backward-induction algorithm with derivative-free search (GSSPI) to reach Stackelberg equilibrium, balancing AAoI and ASINR under attack. Theoretical modeling of SINR and sensing rate, together with AAoI analysis, is combined with extensive simulations showing convergence and superior performance over baselines, including Nash and GA-based schemes. The approach provides dynamic defense mechanisms for time-sensitive LAWNs, improving reliability for critical ISAC operations in hostile environments.

Abstract

The increasing saturation of terrestrial resources has driven economic activities into low-altitude airspace. These activities, such as air taxis, rely on low-altitude wireless networks, and one key enabling technology is integrated sensing and communication (ISAC). However, in low-altitude airspace, ISAC is vulnerable to channel-access attacks, thereby degrading performance and threatening safety. To address this, we propose a defense framework based on a Stackelberg game. Specifically, we first model the system under attack, deriving metrics for the communication and the sensing to quantify performance. Then, we formulate the interaction as a three-player game where a malicious attacker acts as the leader, while the legitimate drone and ground base station act as followers. Using a backward induction algorithm, we obtain the Stackelberg equilibrium, allowing the defenders to dynamically adjust their strategies to mitigate the attack. Simulation results verify that the proposed algorithm converges to a stable solution and outperforms existing baselines, ensuring reliable ISAC performance for critical low-altitude applications.

Figures (4)

• Figure 1: System model of the LAWNs under channel access attack. Here, the malicious UAV first launches an attack by injecting additional noise to the wireless channel. In response, the legitimate UAV with RIS and BS respectively adjust the gain and sensing data generation rate to mitigate the impact of the attack, thereby maintaining the ISAC performance.
• Figure 2: The modeling of the AoI metric.
• Figure 3: Convergence and performance analysis of the proposed Stackelberg game-based defense algorithm. (a) Convergence behavior of the utilities of all players. (b) Attacker’s utility versus the number of RIS elements. (c) Attacker’s utility versus the number of transmit antennas. (d) Attacker’s utility versus the user distribution radius with different equilibriums.
• Figure 4: Performance comparison of the proposed and GA-based optimization schemes. (a) The attacker's utility with different SI coefficients and attacking noise. (b) The RIS's utility with different SI coefficients and attacking noise. (c) The BS's utility with different SI coefficients and attacking noise.