scholarly journals Optimal Network Defense Strategy Selection Method Based on Evolutionary Network Game

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaohu Liu ◽  
Hengwei Zhang ◽  
Yuchen Zhang ◽  
Lulu Shao
Keyword(s):  
Transition Probability ◽  
Evolutionary Game ◽  
Evolutionary Process ◽  
Learning Object ◽  
Game Model ◽  
Strategy Selection ◽  
Learning Mechanism ◽  
Defense Strategy ◽  
Network Game ◽  
Attack And Defense

The basic hypothesis of evolutionary game theory is that the players in the game possess limited rationality. The interactive behavior of players can be described by a learning mechanism that has theoretical advantages in modeling the network security problem in a real society. The current network security evolutionary game model generally adopts a replicator dynamic learning mechanism and assumes that the interaction between players in the group conforms to the characteristics of uniform mixed distribution. However, in an actual network attack and defense scenario, the players in the game have limited learning capability and can only interact with others within a limited range. To address this, we improved the learning mechanism based on the network topology, established the learning object set based on the learning range of the players, used the Fermi function to calculate the transition probability to the learning object strategy, and employed random noise to describe the degree of irrational influence in the learning process. On this basis, we built an attack and defense evolutionary network game model, analyzed the evolutionary process of attack and defense strategy, solved the evolution equilibrium, and designed a defense strategy selection algorithm. The effectiveness of the model and method is verified by conducting simulation experiments for the transition probability of the players and the evolutionary process of the defense group strategy.

scholarly journals Defense Strategy Selection Model Based on Multistage Evolutionary Game Theory

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.

scholarly journals Study on Stochastic Differential Game Model in Network Attack and Defense

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.

scholarly journals MAIAD: A Multistage Asymmetric Information Attack and Defense Model Based on Evolutionary Game Theory

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 215 ◽  
Author(s):  
Yu Yang ◽  
Bichen Che ◽  
Yang Zeng ◽  
Yang Cheng ◽  
Chenyang Li
Keyword(s):  
Game Theory ◽  
Asymmetric Information ◽  
Evolutionary Game Theory ◽  
Rapid Development ◽  
Evolutionary Game ◽  
Defense Strategy ◽  
Security Issues ◽  
Defense Model ◽  
Attack And Defense ◽  
Systems Simulation

With the rapid development and widespread applications of Internet of Things (IoT) systems, the corresponding security issues are getting more and more serious. This paper proposes a multistage asymmetric information attack and defense model (MAIAD) for IoT systems. Under the premise of asymmetric information, MAIAD extends the single-stage game model with dynamic and evolutionary game theory. By quantifying the benefits for both the attack and defense, MAIAD can determine the optimal defense strategy for IoT systems. Simulation results show that the model can select the optimal security defense strategy for various IoT systems.

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

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.

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

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3014
Author(s):  
Pengxi Yang ◽  
Fei Gao ◽  
Hua Zhang
Keyword(s):  
Evolutionary Game ◽  
Stable State ◽  
Payoff Matrix ◽  
Game Model ◽  
Network Attack ◽  
Evolutionary Stable Strategies ◽  
Player Game ◽  
Attack And Defense ◽  
Mathematical Derivation

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.

scholarly journals Evolutionary Game Analysis of Capital-Constrained Supplier’s and Manufacturer’s Financing Schemes

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Suyong Zhang ◽  
Panos. M. Pardalos ◽  
Xiaodan Jiang
Keyword(s):  
Financial Risk ◽  
Evolutionary Game ◽  
Interaction Mechanism ◽  
Game Model ◽  
Strategy Selection ◽  
Game Analysis ◽  
Purchase Order ◽  
Financing Strategy ◽  
Theoretical Results ◽  
Financing Schemes

Purchase order financing (POF) and buyer direct financing (BDF) are both innovative financing schemes aiming to help financial constrained suppliers secure financing for production. In this paper, we investigate the interaction mechanism between suppliers’ financing strategy selection and manufacturers’ loans offering strategy adoption under two innovative financing schemes. We developed an evolutionary game model to effectively investigate the interaction mechanism between suppliers and manufacturers and analyzed the evolutionary stable strategies of the game model. Then we used system dynamics to present the performance of the evolutionary game model and took a sensitivity analysis to verify the theoretical results. The main conclusions are as follows: in the supply chain, to deal with the noncooperation among suppliers and manufacturers on innovative financing schemes, the revenue of manufacturers, the rate of manufacturer loan, and the proper financial risk factor should be relatively high.

scholarly journals Asymmetric conflict: Structures, strategies, and settlement

2019 ◽  
Vol 42 ◽  
Author(s):  
Carsten K. W. De Dreu ◽  
Jörg Gross
Keyword(s):  
Evolutionary Game ◽  
Coordination Games ◽  
Status Quo ◽  
Strategy Selection ◽  
Asymmetric Conflict ◽  
Target Article ◽  
Subordinate Means ◽  
Attack And Defense ◽  
And Behavior ◽  
Asymmetric Game

Abstract Our target article modeled conflict within and between groups as an asymmetric game of strategy and developed a framework to explain the evolved neurobiological, psychological, and sociocultural mechanisms underlying attack and defense. Twenty-seven commentaries add insights from diverse disciplines, such as animal biology, evolutionary game theory, human neuroscience, psychology, anthropology, and political science, that collectively extend and supplement this model in three ways. Here we draw attention to the superordinate structure of attack and defense, and its subordinate means to meet the end of status quo maintenance versus change, and we discuss (1) how variations in conflict structure and power disparities between antagonists can impact strategy selection and behavior during attack and defense; (2) how the positions of attack and defense emerge endogenously and are subject to rhetoric and propaganda; and (3) how psychological and economic interventions can transform attacker-defender conflicts into coordination games that allow mutual gains and dispute resolution.

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