采用双异质群体演化博弈的网络安全防御决策方法

张恩宁; 王刚; 马润年; 伍维甲; 严丽娜

doi:10.7652/xjtuxb202109020

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采用双异质群体演化博弈的网络安全防御决策方法

更新时间：2025-07-09

- 采用双异质群体演化博弈的网络安全防御决策方法
- Network Security Defense Decision Making Method Based on Dual Heterogeneous Population Evolutionary Game Model
- 西安交通大学学报 2021年55卷第9期页码：178-188
- 作者机构：
  
  空军工程大学信息与导航学院,西安,710077
- 作者简介：
- 基金信息：
  
  : 2021-03-09。作者简介: 张恩宁(1998—),男,硕士生;王刚(通信作者),男,教授。基金项目: 国家自然科学基金资助项目(61703420)
- DOI：10.7652/xjtuxb202109020
  中图分类号： TP399
- 网络首发：2021-09-10，
  
  纸质出版：2021
- 稿件说明：
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张恩宁, 王刚, 马润年, 等. 采用双异质群体演化博弈的网络安全防御决策方法[J]. 西安交通大学学报, 2021,55(9):178-188.

Network Security Defense Decision Making Method Based on Dual Heterogeneous Population Evolutionary Game Model[J]. 2021, 55(9): 178-188.
张恩宁, 王刚, 马润年, 等. 采用双异质群体演化博弈的网络安全防御决策方法[J]. 西安交通大学学报, 2021,55(9):178-188. DOI： 10.7652/xjtuxb202109020.

Network Security Defense Decision Making Method Based on Dual Heterogeneous Population Evolutionary Game Model[J]. 2021, 55(9): 178-188. DOI： 10.7652/xjtuxb202109020.

摘要

为提高网络安全防御决策的精准度

提出了采用双异质群体演化博弈的网络安全防御决策方法。针对网络攻防的竞争型决策特点

引入生物学种群异质概念

提出双异质群体演化博弈模型; 将博弈群体对于博弈历史经验的反思机制模型化为泊松过程

改进经典复制动态方程

提出反思能力是提升模型决策速度的关键因素; 结合网络安全防御单次决策的确定性需求和传统纳什均衡解的局限性

提出严格纳什均衡是双异质群体演化博弈稳定的充要条件

通过引入全局单调的势函数

给出了博弈模型存在严格纳什均衡的稳定性证明和仿真验证。理论分析和仿真结果表明

所提模型能够克服经典模型中50%概率的最优策略事实偏差

且当博弈群体的反思能力为1.5时

防御群体决策趋于稳定的速率提升了151%。

Abstract

To improve the accuracy of network security defense decision

according to the characteristics of competitive decision and the limitations of traditional Nash equilibrium(NE)

the heterogeneous population evolutionary game model and decision method of network security defense are proposed. Analyzing the difference in decision behavior standards between the attackers and defenders

and introducing the biological population concept to distinguish the two sides as different game populations

a dual heterogeneous population evolutionary game model of network security defense is constructed. Then an improved replicator dynamic equation is proposed based on the reflection mechanism. Considering the deterministic demand of network defense decision-making and the limitations of NE

introducing the potential function and designing the stabilized pure strategy

the optimal defense pure strategy selection algorithm is proposed. It is concluded that a strict NE is just the necessary and sufficient condition for dual heterogeneous population evolutionary game stability

and the stability proof and simulation analysis are carried out for the strict NE combining with the global monotone property of potential function. The theoretical analysis and simulations show that the proposed game model and decision-making method can ensure stable convergence of the defense strategy

and provide a practical and credible defense strategy for the precise decision-making of actual network attack and defense.

关键词

Keywords

references

FALCO G, ELING M, JABLANSKI D, et al. Cyber risk research impeded by disciplinary barriers [J]. Science, 2019, 366(6469): 1066-1069.

SANDHOLM W H. Evolutionary game theory [M]∥ Complex Social and Behavioral Systems. Cham, Germany: Springer, 2020: 573-608.

ALPCAN T, BASAR T. Network security: a decision and game-theoretic approach [M]. Cambridge, UK: Cambridge University Press, 2010: 20-34.

CHOWDHURY S M. The attack and defense mechanisms: perspectives from behavioral economics and game theory [J]. The Behavioral and Brain Sciences, 2019, 42: e121.

DO C T, TRAN N H, HONG C, et al. Game theory for cyber security and privacy [J]. ACM Computing Surveys, 2017, 50(2): 1-37.

TOSH D, SENGUPTA S, KAMHOUA C A, et al. Establishing evolutionary game models for CYBer security information EXchange(CYBEX)[J]. Journal of Computer and System Sciences, 2018, 98: 27-52.

蒋侣, 张恒巍, 王晋东. 基于信号博弈的移动目标防御最优策略选取方法 [J]. 通信学报, 2019, 40(6): 128-137.

JIANG Lyu, ZHANG Hengwei, WANG Jindong. Optimal strategy selection method for moving target defense based on signaling game [J]. Journal on Communications, 2019, 40(6): 128-137.

谭晶磊, 张恒巍, 张红旗, 等. 基于Markov时间博弈的移动目标防御最优策略选取方法 [J]. 通信学报, 2020, 41(1): 42-52.

TAN Jinglei, ZHANG Hengwei, ZHANG Hongqi, et al. Optimal strategy selection approach of moving target defense based on Markov time game [J]. Journal on Communications, 2020, 41(1): 42-52.

张恒巍, 李涛, 黄世锐. 基于攻防微分博弈的网络安全防御决策方法 [J]. 电子学报, 2018, 46(6): 1428-1435.

ZHANG Hengwei, LI Tao, HUANG Shirui. Network defense decision-making method based on attack-defense differential game [J]. Acta Electronica Sinica, 2018, 46(6): 1428-1435.

黄健明, 张恒巍, 王晋东, 等. 基于攻防演化博弈模型的防御策略选取方法 [J]. 通信学报, 2017, 38(1): 168-176.

HUANG Jianming, ZHANG Hengwei, WANG Jindong, et al. Defense strategies selection based on attack-defense evolutionary game model [J]. Journal on Communications, 2017, 38(1): 168-176.

YANG Yu, CHE Bichen, ZENG Yang, et al. MAIAD: a multistage asymmetric information attack and defense model based on evolutionary game theory [J]. Symmetry, 2019, 11(2): 215.

GHANNADPOUR S F, ZANDIYEH F. A new game-theoretical multi-objective evolutionary approach for cash-in-transit vehicle routing problem with time windows(a real life case)[J]. Applied Soft Computing, 2020, 93: 106378.

YUAN Huiqun, BI Ya, FU Hanchi, et al. Stability analysis of supply chain in evolutionary game based on stability theory of nonlinear differential equation [J]. Alexandria Engineering Journal, 2020, 59(4): 2331-2337.

黄健明, 张恒巍. 基于随机演化博弈模型的网络防御策略选取方法 [J]. 电子学报, 2018, 46(9): 2222-2228.

HUANG Jianming, ZHANG Hengwei. A method for selecting defense strategies based on stochastic evolutionary game model [J]. Acta Electronica Sinica, 2018, 46(9): 2222-2228.

黄健明, 张恒巍. 基于改进复制动态演化博弈模型的最优防御策略选取 [J]. 通信学报, 2018, 39(1): 170-182.

HUANG Jianming, ZHANG Hengwei. Improving replicator dynamic evolutionary game model for selecting optimal defense strategies [J]. Journal on Communications, 2018, 39(1): 170-182.

朱建明, 宋彪, 黄启发. 基于系统动力学的网络安全攻防演化博弈模型 [J]. 通信学报, 2014, 35(1): 54-61.

ZHU Jianming, SONG Biao, HUANG Qifa. Evolution game model of offense-defense for network security based on system dynamics [J]. Journal on Communications, 2014, 35(1): 54-61.

王增光, 卢昱, 李玺. 基于不完全信息博弈的军事信息网络主动防御策略选取 [J]. 兵工学报, 2020, 41(3): 608-617.

WANG Zengguang, LU Yu, LI Xi. Active defense strategy selection of military information network based on incomplete information game [J]. Acta Armamentarii, 2020, 41(3): 608-617.

刘江, 张红旗, 刘艺. 基于不完全信息动态博弈的动态目标防御最优策略选取研究 [J]. 电子学报, 2018, 46(1): 82-89.

LIU Jiang, ZHANG Hongqi, LIU Yi. Research on optimal selection of moving target defense policy based on dynamic game with incomplete information [J]. Acta Electronica Sinica, 2018, 46(1): 82-89.

张恒巍, 黄健明. 基于Markov演化博弈的网络防御策略选取方法 [J]. 电子学报, 2018, 46(6): 1503-1509.

ZHANG Hengwei, HUANG Jianming. Network defense strategy selection method based on Markov evolutionary game [J]. Acta Electronica Sinica, 2018, 46(6): 1503-1509.

LI Yapeng, DENG Ye, XIAO Yu, et al. Attack and defense strategies in complex networks based on game theory [J]. Journal of Systems Science and Complexity, 2019, 32(6): 1630-1640.

LÃ Q D, CHEW Y H, SOONG B H. Potential game theory [M]. Cham, Germany: Springer, 2016: 1-12.

张峰, 吴斌, 王柏, 等. 基于决策偏移的舆论演化动力学模型 [J]. 系统工程理论与实践, 2014, 34(S1): 172-178.

ZHANG Feng, WU Bin, WANG Bai, et al. Decision offset based dynamics opinion model [J]. Systems Engineering-Theory Practice, 2014, 34(S1): 172-178.

SHEYNER O, HAINES J, JHA S, et al. Automated generation and analysis of attack graphs [C]∥Proceedings of the 2002 IEEE Symposium on Security and Privacy. Piscataway, NJ, USA: IEEE, 2002: 273-284.

中国信息安全测评中心. 国家信息安全漏洞库: 漏洞信息 [EB/OL].(2021-02-20)[2021-03-07]. http:∥www.cnnvd.org.cn/web/vulnerability/querylist.tag.

姜伟, 方滨兴, 田志宏, 等. 基于攻防博弈模型的网络安全测评和最优主动防御 [J]. 计算机学报, 2009, 32(4): 817-827.

JIANG Wei, FANG Binxing, TIAN Zhihong, et al. Evaluating network security and optimal active defense based on attack-defense game model [J]. Chinese Journal of Computers, 2009, 32(4): 817-827.

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