Collaborative defense model for crowdsensing networks based on incomplete Information differential games

Abstract Crowdsensing networks face complex security threats due to their open architecture and distributed collaboration model. However, existing defense methods neglect the heterogeneity of sensing nodes and information asymmetry between attackers and defenders, resulting in traditional static defense strategies that struggle to adapt to dynamic adversarial environments. To address this challenge, this paper proposes a dynamic collaborative defense model driven by incomplete information differential games. Firstly, a dynamic differential equation system with random perturbation factors is established to characterize the security state evolution process of sensing nodes. Subsequently, an incomplete information differential game model is constructed to analyze attack and defense behaviors, with a Bayesian strategy inference method designed to infer potential strategy preferences of attackers through real-time attack-defense interaction results and node states. This approach overcomes the limitations of static belief updates in traditional incomplete information games. Finally, a defense decision-making algorithm is designed based on optimal control theory. Simulation results demonstrate that the proposed model accurately characterizes strategic interactions between attackers and defenders while providing real-time defensive strategies based on the inference method, improving defense success rates by 24.2%. Additionally, the model exhibits strong adaptability in dynamic network environments, enhancing network availability by 22.3%.