Evolutionary game and LGPSO for attack-defense confrontation analysis in WSN from macro perspective

Wireless sensor network node security requires effective security mechanisms to protect the security and reliability, so as to ensure that sensor nodes can operate normally and provide reliable data in various environments. The security state change of wireless sensor network nodes is an important research content in the field of wireless sensor security. In this paper, an evolutionary game model is proposed to analyze the security state changes of sensor network nodes from a macro perspective. The existing methods define the parameters subjectively, which leads to the lack of rationality. In this paper, a Levy Flight Global Learning Particle Swarm Optimization (LGPSO) is proposed to calculate the Nash equilibrium of the game and quantify the parameters of the evolutionary game. The method proposed in this paper determines the parameters of the evolutionary game by solving the Nash equilibrium, which makes the model more realistic. The LGPSO has better optimization performance than other algorithms in solving Nash equilibrium problems. The CEC2013 dataset is used to test the performance of LGPSO. The experimental results show that LGPSO is superior to all other algorithms on 68% of the problems. This paper also discusses the influence of the game difficulty coefficient on the evolution process, and experiments show that a larger coefficient is more beneficial to the defender. The macro analysis in this paper provides the method of active defense for the attack-defense confrontation in wireless sensor networks.