Wireless sensors networks (WSNs) play an important role in life. With the development of 5G, its security issues have also raised concerns. Therefore, it is an important topic to study the offense and defense confrontation in WSNs. A complete information static game model is established to analyze the offense and defense confrontation problem of WSNs in 5G. An adaptive equilibrium optimizer algorithm (AEO) based on parameter adaptive strategy is proposed, which can jump out of the local optimal solution better. Experiments show that the optimization ability of AEO outperforms other algorithms on at least $80\%$ of the 23 classical test functions of CEC. The convergence speed of AEO is better in the early stage of population iteration. The optimal offensive and defensive strategy under different offense and defense resources through simulation experiments is analyzed. The conclusion shows that when the offensive resources are large, the offender takes an indiscriminate attack. When the defense resources are small, the defender should defend the most important elements, and when the defense resources are large, the defender should allocate the same resources to defend each element to obtain the maximum benefit. This paper provides new solution ideas for the security problems under the offense and defense game in WSNs.

The development of the 5th Generation Mobile Communication Technology not only brings convenience to people but also brings many network security problems. Based on the static game theory of complete information, a game model of attack and defense with limited resources in heterogeneous networks of Cyber Physical Systems is established. This model analyzes the basic rules of the offensive and defensive strategies of both parties when the offensive and defensive resources are limited in the 5th Generation Mobile Communication Technology network environment. The model can also describe the interaction between attackers and defenders. A novel compact particle swarm optimization algorithm is proposed to solve the difficult problem of solving the Nash equilibrium of this game model. The simulation experiment proves that novel compact particle swarm optimization algorithm has good optimization ability and shows that the algorithm can effectively solve the Nash equilibrium of the model. The simulation experiment provides a strategic reference for the attack-defense game with limited resources.