Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory

Evolutionary game theory is widely applied in network attack and defense. The existing network attack and defense analysis methods based on evolutionary games adopt the bounded rationality hypothesis. However, the existing research ignores that both sides of the game get more information about each...

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Autores principales:	Yanhua Liu, Hui Chen, Hao Zhang, Ximeng Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/94dcf932d5f14ec38e65c77f491098c8
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id	oai:doaj.org-article:94dcf932d5f14ec38e65c77f491098c8
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spelling	oai:doaj.org-article:94dcf932d5f14ec38e65c77f491098c82021-11-22T01:10:51ZDefense Strategy Selection Model Based on Multistage Evolutionary Game Theory1939-012210.1155/2021/4773894https://doaj.org/article/94dcf932d5f14ec38e65c77f491098c82021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4773894https://doaj.org/toc/1939-0122Evolutionary game theory is widely applied in network attack and defense. The existing network attack and defense analysis methods based on evolutionary games adopt the bounded rationality hypothesis. However, the existing research ignores that both sides of the game get more information about each other with the deepening of the network attack and defense game, which may cause the attacker to crack a certain type of defense strategy, resulting in an invalid defense strategy. The failure of the defense strategy reduces the accuracy and guidance value of existing methods. To solve the above problem, we propose a reward value learning mechanism (RLM). By analyzing previous game information, RLM automatically incentives or punishes the attack and defense reward values for the next stage, which reduces the probability of defense strategy failure. RLM is introduced into the dynamic network attack and defense process under incomplete information, and a multistage evolutionary game model with a learning mechanism is constructed. Based on the above model, we design the optimal defense strategy selection algorithm. Experimental results demonstrate that the evolutionary game model with RLM has better results in the value of reward and defense success rate than the evolutionary game model without RLM.Yanhua LiuHui ChenHao ZhangXimeng LiuHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Yanhua Liu Hui Chen Hao Zhang Ximeng Liu Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory
description	Evolutionary game theory is widely applied in network attack and defense. The existing network attack and defense analysis methods based on evolutionary games adopt the bounded rationality hypothesis. However, the existing research ignores that both sides of the game get more information about each other with the deepening of the network attack and defense game, which may cause the attacker to crack a certain type of defense strategy, resulting in an invalid defense strategy. The failure of the defense strategy reduces the accuracy and guidance value of existing methods. To solve the above problem, we propose a reward value learning mechanism (RLM). By analyzing previous game information, RLM automatically incentives or punishes the attack and defense reward values for the next stage, which reduces the probability of defense strategy failure. RLM is introduced into the dynamic network attack and defense process under incomplete information, and a multistage evolutionary game model with a learning mechanism is constructed. Based on the above model, we design the optimal defense strategy selection algorithm. Experimental results demonstrate that the evolutionary game model with RLM has better results in the value of reward and defense success rate than the evolutionary game model without RLM.
format	article
author	Yanhua Liu Hui Chen Hao Zhang Ximeng Liu
author_facet	Yanhua Liu Hui Chen Hao Zhang Ximeng Liu
author_sort	Yanhua Liu
title	Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory
title_short	Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory
title_full	Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory
title_fullStr	Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory
title_full_unstemmed	Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory
title_sort	defense strategy selection model based on multistage evolutionary game theory
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/94dcf932d5f14ec38e65c77f491098c8
work_keys_str_mv	AT yanhualiu defensestrategyselectionmodelbasedonmultistageevolutionarygametheory AT huichen defensestrategyselectionmodelbasedonmultistageevolutionarygametheory AT haozhang defensestrategyselectionmodelbasedonmultistageevolutionarygametheory AT ximengliu defensestrategyselectionmodelbasedonmultistageevolutionarygametheory
_version_	1718418355916898304

Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory

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