Multi-Player Evolutionary Game of Network Attack and Defense Based on System Dynamics

We formalize the adversarial process between defender and attackers as a game and study the non-cooperative evolutionary game mechanism under bounded rationality. We analyze the long-term dynamic process between the attacking and defending parties using the evolutionary stable strategies derived from the evolutionary game model. First, we construct a multi-player evolutionary game model consisting of a defender and multiple attackers, formally describe the strategies, and construct a three-player game payoff matrix. Then, we propose two punishment schemes, i.e., static and dynamic ones. Moreover, through the combination of mathematical derivation with simulation, we obtain the evolutionary stable strategies of each player. Different from previous work, in this paper, we consider the influence of strategies among different attackers. The simulation shows that (1) in the static punishment scheme, increasing the penalty can quickly control the occurrence of network attacks in the short term; (2) in the dynamic punishment scheme, the game can be stabilized effectively, and the stable state and equilibrium values are not affected by the change of the initial values.

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Study on Stochastic Differential Game Model in Network Attack and Defense

Security and Communication Networks ◽

10.1155/2020/3417039 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Xiaotong Xu ◽

Gaocai Wang ◽

Jintian Hu ◽

Yuting Lu

Keyword(s):

Differential Equation ◽

Stochastic Differential Equation ◽

Evolutionary Game ◽

Markov Property ◽

Game Model ◽

Strategy Selection ◽

Network Attack ◽

Disturbance Factor ◽

Attack And Defense ◽

Selection Of

In recent years, evolutionary game theory has been gradually applied to analyze and predict network attack and defense for maintaining cybersecurity. The traditional deterministic game model cannot accurately describe the process of actual network attack and defense due to changing in the set of attack-defense strategies and external factors (such as the operating environment of the system). In this paper, we construct a stochastic evolutionary game model by the stochastic differential equation with Markov property. The evolutionary equilibrium solution of the model is found and the stability of the model is proved according to the knowledge of the stochastic differential equation. And we apply the explicit Euler numerical method to analyze the evolution of the strategy selection of the players for different problem situations. The simulation results show that the stochastic evolutionary game model proposed in this paper can get a steady state and obtain the optimal defense strategy under the action of the stochastic disturbance factor. In addition, compared with other kinds of literature, we can conclude that the return on security investment of this model is better, and the strategy selection of the attackers and defenders in our model is more suitable for actual network attack and defense.

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Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory

Security and Communication Networks ◽

10.1155/2021/4773894 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Yanhua Liu ◽

Hui Chen ◽

Hao Zhang ◽

Ximeng Liu

Keyword(s):

Game Theory ◽

Evolutionary Game Theory ◽

Evolutionary Game ◽

Dynamic Network ◽

Game Model ◽

Strategy Selection ◽

Network Attack ◽

Learning Mechanism ◽

Defense Strategy ◽

Attack And Defense

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.

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A Study of Taxi Service Mode Choice Based on Evolutionary Game Theory

Journal of Advanced Transportation ◽

10.1155/2019/8607942 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17

Author(s):

Zhu Bai ◽

Mingxia Huang ◽

Shuai Bian ◽

Huandong Wu

Keyword(s):

Game Theory ◽

Evolutionary Game Theory ◽

Mode Choice ◽

Evolutionary Game ◽

Payoff Matrix ◽

Game Model ◽

Stable States ◽

Taxi Service ◽

Service Mode ◽

The Impact

The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management.

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Evolutionary game analysis of information sharing by institutional investors

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219143 ◽

2021 ◽

pp. 1-9

Author(s):

Hua Li ◽

Qingqing Lou ◽

Qiubai Sun ◽

Bowen Li

Keyword(s):

Information Sharing ◽

Institutional Investors ◽

Information Exchange ◽

Evolutionary Game ◽

Stable State ◽

Point Of View ◽

Game Model ◽

Game Analysis ◽

Relationship Network ◽

The Relationship

In order to solve the conflict of interests of institutional investors, this paper uses evolutionary game model. From the point of view of information sharing, this paper discusses four different situations. Only when the sum of risk and cost is less than the penalty of free riding, the evolution of institutional investors will eventually incline to the stable state of information sharing. That is, the phenomenon of hugging. The research shows that the institutional investors are not independent of each other, but the relationship network of institutional investors for the purpose of information exchange. The content of this paper enriches the research on information sharing of institutional investors.

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Generation Method of Power Network Security Defense Strategy Based on Markov Decision Process

Distributed Generation & Alternative Energy Journal ◽

10.13052/dgaej2156-3306.3635 ◽

2021 ◽

Author(s):

Wang Yang ◽

Liu Dong ◽

Wang Dong ◽

Xu Chun

Keyword(s):

Network Security ◽

Response Time ◽

Markov Decision Process ◽

Decision Process ◽

Evolutionary Game ◽

Power Network ◽

Network Attack ◽

Defense Strategy ◽

Markov Decision ◽

Attack And Defense

Aiming at the problem that the current generation method of power network security defense strategy ignores the dependency relationship between nodes, resulting in closed-loop attack graph, which makes the defense strategy not generate attack path, resulting in poor defense effect and long generation response time of power network security defense strategy, a generation method of power network security defense strategy based on Markov decision process is proposed. Based on the generation of network attack and defense diagram, the paper describes the state change of attack network by using Markov decision-making process correlation principle, introduces discount factor, calculates the income value of attack and defense game process, constructs the evolutionary game model of attack and defense, solves the objective function according to the dynamic programming theory, obtains the optimal strategy set and outputs the final results, and generates the power network security defense strategy. The experimental results show that the proposed method has good defense effect and can effectively shorten the generation response time of power network security defense strategy.

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An Analysis of Quality Liability Insurance for Prefabricated Components Using Evolutionary Game Theory

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2020-0012 ◽

2021 ◽

Author(s):

Yan Liu ◽

Chenyao Lv ◽

Hong Xian Li ◽

Yan Li ◽

Zhen Lei ◽

...

Keyword(s):

Game Theory ◽

Evolutionary Game Theory ◽

Evolutionary Game ◽

Payoff Matrix ◽

Liability Insurance ◽

Insurance Companies ◽

Social Benefits ◽

Simulation Calculation ◽

The Government

Managing quality risks of prefabricated components is one of the challenges for prefabricated construction. The Quality Liability Insurance for Prefabricated Components (QLIPC) is an effective approach to transfer such risks; however, limited research has been conducted regarding the development of QLIPC. This study introduces an Evolutionary Game Theory (EGT)-based approach incorporating decisions from both the government and insurance companies. In the EGT model, a payoff matrix under disparate strategies is constructed, and the evolutionary stable strategies (ESS) are deduced. The simulation calculation is then carried out by MATLAB using sample virtual data to demonstrate the analysis. The results show that the government should act as the game promoter because the QLIPC can reduce governance cost and has significant social benefits. This research contributes a theoretical framework to analyze the QLIPC development using the EGT theory, and it could help the government to make long-term strategies for developing the QLIPC market.

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Game model of enterprises and government based on the tax preference policy for energy conservation and emission reduction

Filomat ◽

10.2298/fil1615963z ◽

2016 ◽

Vol 30 (15) ◽

pp. 3963-3974 ◽

Cited By ~ 1

Author(s):

Guoxing Zhang ◽

Zhenhua Zhang ◽

Yongjing Cui ◽

Chun Yuan

Keyword(s):

Energy Conservation ◽

Emission Reduction ◽

Evolutionary Game ◽

Optimal Number ◽

Signaling Game ◽

Game Model ◽

Tax Preference ◽

Single Period ◽

Separating Equilibrium

In recent years, greater efforts in tax preference policy for energy conservation and emission reduction (ECER) have been implemented in our country. Based on the tax preference of enterprise income for comprehensive utilization of resources, the constraints to achieve completely successful equilibrium are studied in the single period and multiple periods. In the single period, the key to achieve separating equilibrium is analyzed carefully by constructing a signaling game model of enterprises and government on tax preference of enterprise income. In the multiple periods, with the stochastic evolutionary game model based on the stochastic differential equation (SDE) theory, the constraints of keeping the separating equilibrium stable and continuing in a long term will be further investigated. It gives the optimal number of tax preference of enterprise income, camouflage cost and expected cost of risk under the state of separating equilibrium. The optimal result of completely successful equilibrium is obtained in single period by the analysis of numerical example for enterprises and government signaling game model. The simulation experiment is successfully finished to test the effectiveness of the stochastic evolutionary game model by using mathematical software MATLAB.

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Network Attack and Defense Modeling and System Security Analysis: A Novel Approach Using Stochastic Evolutionary Game Petri Net

Security and Communication Networks ◽

10.1155/2021/4005877 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Zenan Wu ◽

Liqin Tian ◽

Yi Zhang ◽

Yan Wang ◽

Yuquan Du

Keyword(s):

Petri Net ◽

Evolutionary Game ◽

Security Analysis ◽

System Security ◽

Analysis Method ◽

Network Attack ◽

Network Connection ◽

Novel Approach ◽

Average System ◽

Attack And Defense

At present, most network security analysis theory assumes that the players are completely rational. However, this is not consistent with the actual situation. In this paper, based on the effectiveness constraints on both sides with network attack and defense, with the help of stochastic Petri net and evolutionary game theory, the Petri net model of network attack and defense stochastic evolutionary game is reconstructed, the specific definition of the model is given, and the modeling method is given through the network connection relationship and attack and defense strategy set. Using this model, a quantitative analysis of network attack events is carried out to solve a series of indicators related to system security, namely, attack success rate, average attack time, and average system repair time. Finally, the proposed model and analysis method are applied to a classic network attack and defense process for experimental analysis, and the results verify the rationality and accuracy of the model and analysis method.

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Evolutionary game analysis of Chinese cross border logistics enterprises adopting blockchain technology in supply chain

E3S Web of Conferences ◽

10.1051/e3sconf/202127503078 ◽

2021 ◽

Vol 275 ◽

pp. 03078

Author(s):

KunYang Liu ◽

Yong Zhang

Keyword(s):

Supply Chain ◽

Evolutionary Game ◽

Game Model ◽

Game Analysis ◽

Technological Revolution ◽

Cross Border ◽

Technical Ability ◽

Blockchain Technology ◽

Logistics Enterprises ◽

Mathematical Derivation

Blockchain technology is considered to be the representative technology of the fourth technological revolution, and logistics and supply chain field has been considered as the main application direction in the next step by industry and academia. In order to study the behavior and intention of Chinese cross-border logistics enterprises to introduce blockchain into internal supply chain, this paper constructs a model of cross-border logistics enterprises adopting blockchain technology on the basis of bounded rationality based on the game model, this paper attempts to find out the factors that affect the introduction of blockchain into the internal supply chain through mathematical derivation and numerical analysis. The game results show that the willingness of cross-border logistics enterprises to adopt blockchain is closely related to the technical ability of blockchain, the incremental performance after the introduction of blockchain, the conversion cost of blockchain technology, and other factors. Enterprises should coordinate various factors and choose to adopt blockchain technology according to their own situation.

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The Impact of Penalty and Subsidy Mechanisms On The Decisions of Governments, Businesses, and Consumers During COVID-19 ——Tripartite Evolutionary Game Theory Analysis

10.21203/rs.3.rs-1012076/v1 ◽

2021 ◽

Author(s):

Yuxun Zhou ◽

Rahman Mohammad Mafizur ◽

Khanam Rasheda ◽

Brad R. Taylor

Keyword(s):

Game Theory ◽

Evolutionary Game Theory ◽

Virus Disease ◽

Evolutionary Game ◽

Initial Point ◽

Evolutionarily Stable Strategies ◽

Corona Virus ◽

Evolutionary Stable Strategies ◽

The Impact

Abstract Purpose – Based on the fact that punishment and subsidy mechanisms affect the anti-epidemic incentives of major participants in a society, the issue of this paper is how the penalty and subsidy mechanisms affect the decisions of governments, businesses, and consumers during Corona Virus Disease 2019. The goal of this paper is to understand strategic selections from governments, enterprises, and consumers to maximize their respective utility during Corona Virus Disease 2019, and the impact of penalty and subsidy mechanism on the decisions of governments, businesses, and consumers.Design/Methodology/approach - This paper proposes a tripartite evolutionary game theory, involving governments, businesses, and consumers, to firstly analyze the evolutionary stable strategies and to secondly analyze the impact of penalty and subsidy mechanism on their strategy selection during Corona Virus Disease 2019. Thirdly, this paper uses numerical analysis to simulate the strategy formation process of governments, enterprises, and consumers in Japan and India based on their different penalty and subsidy mechanism.Findings – This paper suggests that there are four evolutionarily stable strategies corresponding to the actual anti-epidemic situations of different countries in reality. We find that different subsidy and penalty mechanisms lead to different evolutionary stable strategies. If governments, enterprises, and consumers fighting the pandemic together, the government need to set a low subsidy mechanism and a high penalty mechanism.Originality/value - There are some limitations in the literature, such as long term strategies, rational hypothesis, and convergence path analysis in higher dimensional evolutionary game theory. This paper fills the gap and extends the theory of COVID-19 management theory. Firstly, this paper has important practical significance. This paper finds out the long-term equilibrium strategies of governments, businesses, and consumers under Corona Virus Disease 2019, which can provide an important theoretical and decision-making basis for pandemic prevention and control. Secondly, our paper extends the analytical paradigm of the tripartite evolutionary game theory. We extend the analysis of the dynamic process from the initial point to the convergence point and make a theoretical contribution to the development of high-dimensional evolutionary game theory.

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