Analysis for Network Attack-Defense Based on Stochastic Game Model

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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GAME MODEL OF ONTOLOGICAL PROJECT SUPPORT

Radio Electronics Computer Science Control ◽

10.15588/1607-3274-2021-1-17 ◽

2021 ◽

Vol 1 (1) ◽

pp. 172-183

Author(s):

P. Kravets ◽

V. Lytvyn ◽

V. Vysotska

Keyword(s):

Nash Equilibrium ◽

Virtual Teams ◽

Stochastic Approximation ◽

Intelligent Agents ◽

Stochastic Game ◽

Equilibrium Points ◽

Experimental Studies ◽

Game Model ◽

Network Marketing ◽

Nash Equilibrium Points

Context. In today’s information society with advanced telecommunications through mobile devices and computer networks, it is important to form a variety of virtual organizations and communities. Such virtual associations of people by professional or other interests are designed to quickly solve various tasks: to perform project tasks, create startups to attract investors, network marketing, distance learning, solving complex problems in science, economics and public administration , construction of various Internet services, discussion of political and social processes, etc. Objective of the study is to develop an adaptive Markov recurrent method based on the stochastic approximation of the modified condition of complementary non-rigidity, valid at Nash equilibrium points for solving the problem of game coverage of projects. Method. In this work the multiagent game model for formation of virtual teams of executors of projects on the basis of libraries of subject ontologies is developed. The competencies and abilities of agents required to carry out projects are specified by sets of ontologies. Intelligent agents randomly, simultaneously and independently choose one of the projects at discrete times. Agents who have chosen the same project determine the current composition of the team of its executors. For agents’ teams, a current penalty is calculated for insufficient coverage of competencies by the combined capabilities of agents. This penalty is used to adaptively recalculate mixed player strategies. The probabilities of selecting those teams whose current composition has led to a reduction in the fine for non-coverage of ontologies are increasing. During the repetitive stochastic game, agents will form vectors of mixed strategies that will minimize average penalties for non-coverage of projects. Results. For solve the problem of game coverage of projects, an adaptive Markov recurrent method based on the stochastic approximation of the modified condition of complementary non-rigidity, valid at Nash equilibrium points, was developed. Conclusions. Computer simulation confirmed the possibility of using the stochastic game model to form teams of project executors with the necessary ontological support in conditions of uncertainty. The convergence of the game method is ensured by compliance with the fundamental conditions and limitations of stochastic optimization. The reliability of experimental studies is confirmed by the repeatability of the results obtained for different sequences of random variables.

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Integrating Demand Response and Renewable Energy In Wholesale Market

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/53 ◽

2018 ◽

Cited By ~ 4

Author(s):

Chaojie Li ◽

Chen Liu ◽

Xinghuo Yu ◽

Ke Deng ◽

Tingwen Huang ◽

...

Keyword(s):

Renewable Energy ◽

Demand Response ◽

Electricity Market ◽

Stochastic Game ◽

Renewable Energy Sources ◽

Game Model ◽

Renewable Energy Resources ◽

Wholesale Market ◽

Policy Makers ◽

Optimal Energy

Demand response (DR) can provide a cost-effect approach for reducing peak loads while renewable energy sources (RES) can result in an environmental-friendly solution for solving the problem of power shortage. The increasingly integration of DR and renewable energy bring challenging issues for energy policy makers, and electricity market regulators in the main power grid. In this paper, a new two-stage stochastic game model is introduced to operate the electricity market, where Stochastic Stackelberg-Cournot-Nash (SSCN) equilibrium is applied to characterize the optimal energy bidding strategy of the forward market and the optimal energy trading strategy of the spot market. To obtain a SSCN equilibrium, sampling average approximation (SAA) technique is harnessed to address the stochastic game model in a distributed way. By this game model, the participation ratio of demand response can be significantly increased while the unreliability of power system caused by renewable energy resources can be considerably reduced. The effectiveness of proposed model is illustrated by extensive simulations.

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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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