基于前景理论的网络安全防御策略优化方法

doi:10.16381/j.cnki.issn1003-207x.2023.1778

Abstract

Abstract:

With regards to cases of real network security management， the choice of security defence strategy is a common problem. However， the existing methods to choose security defence strategy is limited by the network administrators’ cognition and network topology. Therefore， an optimal way is proposed to select internet security defence strategy based on prospect theory. At first， several problems in optimizing defence strategy selection for sub-networks and calculates comprehensive prospect utility values for both the offensive and the defensive are described. Then， an optimal model is constructed for the choice of internet security strategy based on the prospect theory. This model considers the cost of defence and topology of network. Later， the optimal defence strategies are computed with different defence costs， network topology and the limited rationality of the defenders. Meanwhile， the influence law of defender rationality degree parameters on defense strategy optimization are researched. The optimal defence strategies model expands the application of offensive and defensive games， not only offers a new way to optimize the option of network security defence strategy against limited rationality， but also expands the application field of security game. Finally， a network security problem in logistic system is demonstrated as an example to prove the validity of the proposed method.

Key words: prospect theory, security game, defense strategy, network security

CLC Number:

F592

Gaofeng Yu,Dengfeng Li. Research on Network Security Selecting Optimal Defense Strategies Based on Prospect Theory[J]. Chinese Journal of Management Science, 2026, 34(2): 156-163.

Figures/Tables 12

子网络	节点	网关
$w 1$	$r 1, r 2, r 3, r 4$	$g 1$
$w 2$	$r 5, r 6$	$g 2$
$w 3$	$r 7, r 8$	$g 3$

网关	$D (g j \| p)$	$D (g j \| u)$
$g 1$	20	-30
$g 2$	15	-15
$g 3$	15	-10

节点	$D (r i \| p)$	$D (r i \| u)$
$r 1$	1	-1
$r 2$	5	-5
$r 3$	10	-12
$r 4$	1	-1
$r 5$	1	-1
$r 6$	10	-10
$r 7$	1	-1
$r 8$	1	-1
$r 9$	10	-10

节点	网关
节点	$g 1$	$g 2$	$g 3$
$r 1$	0
$r 2$	0.2
$r 3$	0.7
$r 4$	0.9
$r 5$		0.2
$r 6$		0.8
$r 7$		0.9
$r 8$			0
$r 9$			1

		防御成本
		10	15	20	25	30
节点和网关的安全水平	$g 1$	0.492	1.0	1.0	1.0	1
	$g 2$	0.1	0.14	0.68	1	1
	$g 3$	0.1	0.1	0.1	0.1	0.52
	$r 1$	0.1	0.1	0.1	0.1	1
	$r 2$	1	1	1	1	1
	$r 3$	1	1	1	1	1
	$r 4$	0.54	1	1	1	1
	$r 5$	0.12	0.13	0.24	1	1
	$r 6$	1	1	1	1	1
	$r 7$	0.19	0.23	0.72	1	1
	$r 8$	0.1	0.1	0.1	0.7	1
	$r 9$	0.2	0.2	0.2	1	1
系统安全水平		4.94	5.99	7.14	9.9	11.52

		防御成本
		10	15	20	25	30
网关	$g 1$	4.92	10	10	10	10
	$g 2$	1	1.4	6.8	10	10
	$g 3$	1	1	1	1	5.2
节点	$r 1$	0.1	0.1	0.1	0.1	1
	$r 2$	0.9	0.8	0.8	0.8	0.8
	$r 3$	0.66	0.3	0.3	0.3	0.3
	$r 4$	0.1	0.1	0.1	0.1	0.1
	$r 5$	0.1	0.1	0.1	0.8	0.8
	$r 6$	0.92	0.88	0.45	0.62	0.2
	$r 7$	0.1	0.1	0.1	0.2	0.1
	$r 8$	0.1	0.1	0.1	0.7	1
	$r 9$	0.1	0.1	0.1	0.9	0.47
前景效用值		-55.3	-22.1	-0.9	18.3	34.06

		防御成本
		10	15	20	25	30
节点和网关的安全水平	$g 1$	0.444	0.94	1	1.0	1
	$g 2$	0.1	0.1	0.54	1	1
	$g 3$	0.1	0.1	0.1	0.14	0.64
	$r 1$	0.1	0.1	0.1	0.1	0.1
	$r 2$	1	1	1	1	1
	$r 3$	1	1	1	1	1
	$r 4$	0.1	0.1	0.1	0.1	1
	$r 5$	0.1	0.1	0.1	0.1	0.1
	$r 6$	1	1	1	1	0.1
	$r 7$	0.1	0.1	0.1	0.1	0.1
	$r 8$	0.1	0.1	0.1	0.1	0.1
	$r 9$	0.1	0.1	0.1	0.1	0.1
最优收益值		-16.3	8.7	24.9	39.7	52.2
系统安全水平		4.24	4.74	5.24	5.74	6.24

References 26

[1]	杨善林，周开乐，张强，等. 互联网的资源观［J］. 管理科学学报， 2016， 19（1）： 1-11.
	Yang S L， Zhou K L， Zhang Q， et al. A resource view of the Internet［J］. Journal of Management Sciences in China， 2016， 19（1）： 1-11.
[2]	杨善林，李霄剑，张强，等. 人工智能与管理变革［J］. 中国管理科学， 2023， 31（6）： 1-11.
	Yang S L， Li X J， Zhang Q， et al. Artificial intelligence and management transformation［J］. Chinese Journal of Management Science， 2023， 31（6）： 1-11.
[3]	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 W， Fang B X， Tian Z H， 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.
[4]	Li Y， Quevedo D E， Dey S， et al. A game-theoretic approach to fake-acknowledgment attack on cyber-physical systems［J］. IEEE Transactions on Signal and Information Processing Over Networks， 2017， 3（1）： 1-11.
[5]	Esmalifalak M， Shi G， Han Z， et al. Bad data injection attack and defense in electricity market using game theory study［J］. IEEE Transactions on Smart Grid， 2013， 4（1）： 160-169.
[6]	Wu J， Ota K， Dong M， et al. Big data analysis-based security situational awareness for smart grid［J］. IEEE Transactions on Big Data， 2018， 4（3）： 408-417.
[7]	方玲，仲伟俊，梅姝娥. 安全等级对信息系统安全技术策略的影响研究——以防火墙和IDS技术组合为例［J］. 系统工程理论与实践， 2016， 36（5）： 1231-1238.
	Fang L， Zhong W J， Mei S E. Study on the influence of security rank on the strategy of information system security technology： Taking the example of firewall and IDS technology portfolio［J］. Systems Engineering—Theory & Practice， 2016， 36（5）： 1231-1238.
[8]	刘玉岭，冯登国，吴丽辉，等. 基于静态贝叶斯博弈的蠕虫攻防策略绩效评估［J］. 软件学报， 2012， 23（3）： 712-723.
	Liu Y L， Feng D G， Wu L H， et al. Performance evaluation of worm attack and defense strategies based on static Bayesian game［J］. Journal of Software， 2012， 23（3）： 712-723.
[9]	黄世锐，张恒巍，王晋东，等. 基于定性微分博弈的网络安全威胁预警方法［J］. 通信学报， 2018， 39（8）： 29-36.
	Huang S R， Zhang H W， Wang J D， et al. Network security threat warning method based on qualitative differential game［J］. Journal on Communications， 2018， 39（8）： 29-36.
[10]	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 J L， Zhang H W， Zhang H Q， et al. Optimal strategy selection approach of moving target defense based on Markov time game［J］. Journal on Communications， 2020， 41（1）： 42-52.
[11]	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 J， Zhang H Q， Liu Y. 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.
[12]	梁开荣，李登峰，余高锋. 基于两型博弈的双边链路形成策略优化研究［J］. 系统科学与数学， 2020， 40（9）： 1550-1563.
	Liang K R， Li D F， Yu G F. Research on strategy optimization of bilateral link formation based on biform games［J］. Journal of Systems Science and Mathematical Sciences， 2020， 40（9）： 1550-1563.
[13]	许保光，王蓓蓓，池宏，等. 基于贝叶斯网络的航空安全中不安全信息分析［J］. 中国管理科学， 2020， 28（12）： 118-129.
	Xu B G， Wang B B， Chi H， et al. Insecurity information analysis in civil aviation safety based on Bayesian network［J］. Chinese Journal of Management Science， 2020， 28（12）： 118-129.
[14]	Yang R， Kiekintveld C， Ordóñez F， et al. Improving resource allocation strategies against human adversaries in security games： An extended study［J］. Artificial Intelligence， 2013， 195： 440-469.
[15]	Cheung K F， Bell M G H. Attacker–defender model against quantal response adversaries for cyber security in logistics management： An introductory study［J］. European Journal of Operational Research， 2021， 291（2）： 471-481.
[16]	王元卓，于建业，邱雯，等. 网络群体行为的演化博弈模型与分析方法［J］. 计算机学报， 2015， 38（2）： 282-300.
	Wang Y Z， Yu J Y， Qiu W， et al. Evolutionary game model and analysis methods for network group behavior［J］. Chinese Journal of Computers， 2015， 38（2）： 282-300.
[17]	Hayel Y， Zhu Q. Epidemic protection over heterogeneous networks using evolutionary Poisson games［J］. IEEE Transactions on Information Forensics and Security， 2017， 12（8）： 1786-1800.
[18]	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 J M， Zhang H W， Wang J D， et al. Defense strategies selection based on attack-defense evolutionary game model［J］. Journal on Communications， 2017， 38（1）： 168-176.
[19]	Al-Jaoufi M A A， Liu Y， Zhang Z. An active defense model with low power consumption and deviation for wireless sensor networks utilizing evolutionary game theory［J］. Energies， 2018， 11（5）： 1281.
[20]	张艳楠，孙绍荣. 基于Stackelberg博弈模型的化工企业安全生产管理机制治理研究［J］. 中国管理科学， 2016， 24（3）： 159-168.
	Zhang Y N， Sun S R. Research on safety production management mechanism in enterprise based on Stackelberg game model［J］. Chinese Journal of Management Science， 2016， 24（3）： 159-168.
[21]	余高锋，李登峰. 新零售生态网络安全态势评级混合型变权方法及稳定性研究［J］. 中国管理科学， 2024， 32（10）： 234-243.
	Yu G F， Li D F. The stability and variable weight decision method for rating new retail ecology network security situation［J］. Chinese Journal of Management Science， 2024， 32（10）： 234-243.
[22]	Yu G F. A multi-objective decision method for the network security situation grade assessment under multi-source information［J］. Information Fusion， 2024， 102： 102066.
[23]	雷星，胡笑旋，王国强，等. 基于Stackelberg安全博弈的多无人机边境巡逻问题研究［J］. 系统工程理论与实践， 2023， 43（3）： 889-909.
	Lei X， Hu X X， Wang G Q， et al. Border patrol using multiple unmanned aerial vehicles based on Stackelberg security game［J］. Systems Engineering-Theory & Practice， 2023， 43（3）： 889-909.
[24]	Casorrán C， Fortz B， Labbé M， et al. A study of general and security Stackelberg game formulations［J］. European Journal of Operational Research， 2019， 278（3）： 855-868.
[25]	Bucarey L V， Casorrán C， Labbé M， et al. Coordinating resources in Stackelberg security games［J］. European Journal of Operational Research， 2021， 291（3）： 846-861.
[26]	Tversky A， Kahneman D. Advances in prospect theory： Cumulative representation of uncertainty［J］. Journal of Risk and Uncertainty， 1992， 5（4）： 297-323.

Research on Network Security Selecting Optimal Defense Strategies Based on Prospect Theory

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