Local Conformity-Based Evolutionary Game Modeling of UAV Swarm Under Byzantine Attack
For UAV swarm security, this work provides a theoretical model to understand how local conformity influences the spread of deceptive strategies under Byzantine attacks.
This paper models UAV swarm behavior under Byzantine attacks using graph evolutionary game theory, incorporating local conformity. It derives analytical solutions for deceptive strategy evolution and shows that observation errors weaken attacks, while higher malicious node proportions and attack intensity amplify impacts, with robustness across network topologies.
Leveraging their flexible and efficient deployment capabilities, unmanned aerial vehicle (UAV) swarms have been widely applied in various mission scenarios. However, the open communication environment also exposes them to the threat of Byzantine attacks. Most existing studies assume independent decision-making by each UAV, neglecting that local conformity amplifies false information propagation. This paper constructs an evolutionary game model for UAV swarm under malicious attacks based on graph evolutionary game theory, revealing how local conformity rules govern the spread of deceptive strategies. Using death-birth updating rules, we derive the macroscopic dynamic equation for the fraction of deceptive strategies and the analytical solutions to its evolutionary stable states. Sim ulations reveal observation errors weaken malicious induction, while higher proportions of malicious nodes and greater attack intensity drastically amplify attack impacts. Moreover, the model exhibits strong topological robustness across regular, scale-free and random networks.