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 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 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 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 Research on the Natural Hazard Emergency Cooperation Behavior between Governments and Social Organizations Based on the Hybrid Mechanism of Incentive and Linkage in China

2021 ◽  
Vol 18 (24) ◽  
pp. 13064
Author(s):  
Jida Liu ◽  
Changqi Dong ◽  
Shi An ◽  
Yanan Guo
Keyword(s):  
Local Government ◽  
Natural Hazard ◽  
Evolutionary Game ◽  
Game Model ◽  
Strategy Selection ◽  
Factors Affecting ◽  
Social Organizations ◽  
Evolutionary Trajectories ◽  
Emergency Responses ◽  
Selection Of

Social organizations have become an important component of the emergency management system by virtue of their heterogeneous resource advantages. It is of great significance to explore the interaction between the local government and social organizations and to clarify the key factors affecting the participation of social organizations in natural hazard emergency responses. With the aim of exploring the relationship between the local government and social organizations, based on evolutionary game theory, the emergency incentive game model and the emergency linkage game model of natural hazard emergency responses were constructed. The evolutionary trajectories of the emergency incentive game system and the emergency linkage game system were described by numerical simulation. Meanwhile, the influence mechanism of government decision parameters on the strategy selection of both game subjects was analyzed. The results show that both governmental incentive strategy and linkage strategy can significantly improve the enthusiasm of social organizations for participating in natural hazard emergency responses. Moreover, they could encourage social organizations to choose a positive participation strategy. Nevertheless, over-reliance on incentives reduces the probability of the local government choosing a positive emergency strategy. In addition, we found that, when both game subjects tend to choose a positive strategy, the strategy selection of the local government drives that of social organizations.

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

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.

scholarly journals PUEGM: A Method of User Revenue Selection Based on a Publisher-User Evolutionary Game Model for Mobile Crowdsensing

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2927
Author(s):  
Zihao Shao ◽  
Huiqiang Wang ◽  
Guangsheng Feng
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Low Cost ◽  
Evolutionary Game ◽  
Real Data ◽  
Quality Data ◽  
Game Model ◽  
Mobile Crowdsensing ◽  
Data Quality Assessment ◽  
Selection Of

Mobile crowdsensing (MCS) is a way to use social resources to solve high-precision environmental awareness problems in real time. Publishers hope to collect as much sensed data as possible at a relatively low cost, while users want to earn more revenue at a low cost. Low-quality data will reduce the efficiency of MCS and lead to a loss of revenue. However, existing work lacks research on the selection of user revenue under the premise of ensuring data quality. In this paper, we propose a Publisher-User Evolutionary Game Model (PUEGM) and a revenue selection method to solve the evolutionary stable equilibrium problem based on non-cooperative evolutionary game theory. Firstly, the choice of user revenue is modeled as a Publisher-User Evolutionary Game Model. Secondly, based on the error-elimination decision theory, we combine a data quality assessment algorithm in the PUEGM, which aims to remove low-quality data and improve the overall quality of user data. Finally, the optimal user revenue strategy under different conditions is obtained from the evolutionary stability strategy (ESS) solution and stability analysis. In order to verify the efficiency of the proposed solutions, extensive experiments using some real data sets are conducted. The experimental results demonstrate that our proposed method has high accuracy of data quality assessment and a reasonable selection of user revenue.

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.

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