Open Access

ARTICLE

Evolution Analysis of Network Attack and Defense Situation Based on Game Theory

Haiyan Sun^1,*, Chenglong Shao¹, Jianwei Zhang¹, Kun Wang², Wanwei Huang¹

1 College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450007, China
2 Intelligent Computing Power Research Department, Zhengzhou Xinda Advanced Technology Research Institute, Zhengzhou, 450007, China

* Corresponding Author: Haiyan Sun. Email:

Computers, Materials & Continua 2025, 83(1), 1451-1470. https://doi.org/10.32604/cmc.2025.059724

Received 15 October 2024; Accepted 30 December 2024; Issue published 26 March 2025

Abstract

To address the problem that existing studies lack analysis of the relationship between attack-defense game behaviors and situation evolution from the game perspective after constructing an attack-defense model, this paper proposes a network attack-defense game model (ADGM). Firstly, based on the assumption of incomplete information between the two sides of the game, the ADGM model is established, and methods of payoff quantification, equilibrium solution, and determination of strategy confrontation results are presented. Then, drawing on infectious disease dynamics, the network attack-defense situation is defined based on the density of nodes in various security states, and the transition paths of network node security states are analyzed. Finally, the network zero-day virus attack-defense behaviors are analyzed, and comparative experiments on the attack-defense evolution trends under the scenarios of different strategy combinations, interference methods, and initial numbers are conducted using the NetLogo simulation tool. The experimental results indicate that this model can effectively analyze the evolution of the macro-level network attack-defense situation from the micro-level attack-defense behaviors. For instance, in the strategy selection experiment, when the attack success rate decreases from 0.49 to 0.29, the network destruction rate drops by 11.3%, in the active defense experiment, when the interference coefficient is reduced from 1 to 0.7, the network destruction rate decreases by 7%, and in the initial node number experiment, when the number of initially infected nodes increases from 10 to 30, the network destruction rate rises by 3%.

---

Keywords

---