scholarly journals Stochastic Differential Game-Based Malware Propagation in Edge Computing-Based IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Li Miao ◽  
Shuai Li
Keyword(s):  
Differential Game ◽  
Infinite Horizon ◽  
Optimal Strategies ◽  
Edge Computing ◽  
Game Model ◽  
Stochastic Differential Game ◽  
Equilibrium Solutions ◽  
Stochastic Fluctuations ◽  
Malware Propagation ◽  
Nash Equilibrium Solutions

Internet of Things (IoT) has played an important role in our daily life since its emergence. The applications of IoT cover from the traditional devices to intelligent equipment. With the great potential of IoT, there comes various kinds of security problems. In this paper, we study the malware propagation under the dynamic interaction between the attackers and defenders in edge computing-based IoT and propose an infinite-horizon stochastic differential game model to discuss the optimal strategies for the attackers and defenders. Considering the effect of stochastic fluctuations in the edge network on the malware propagation, we construct the Itô stochastic differential equations to describe the propagation of the malware in edge computing-based IoT. Subsequently, we analyze the feedback Nash equilibrium solutions for our proposed game model, which can be considered as the optimal strategies for the defenders and attackers. Finally, numerical simulations show the effectiveness of our proposed game model.

Nested variational inequalities and related optimal starting–stopping problems

1992 ◽  
Vol 29 (01) ◽  
pp. 104-115 ◽  
Author(s):  
M. Sun
Keyword(s):  
Differential Equation ◽  
Variational Inequalities ◽  
Stochastic Differential Equation ◽  
Differential Game ◽  
Diffusion Model ◽  
Value Function ◽  
Optimal Strategies ◽  
Stochastic Differential Game ◽  
The Value Function

This paper introduces several versions of starting-stopping problem for the diffusion model defined in terms of a stochastic differential equation. The problem could be regarded as a stochastic differential game in which the player can only decide when to start the game and when to quit the game in order to maximize his fortune. Nested variational inequalities arise in studying such a problem, with which we are able to characterize the value function and to obtain optimal strategies.

scholarly journals A Cooperative Stochastic Differential Game of Transboundary Industrial Pollution between Two Asymmetric Nations

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yongxi Yi ◽  
Rongwei Xu ◽  
Sheng Zhang
Keyword(s):  
Differential Game ◽  
Pollution Control ◽  
Industrial Pollution ◽  
Stackelberg Game ◽  
Infinite Horizon ◽  
Pollution Abatement ◽  
Transboundary Pollution ◽  
Stochastic Differential Game ◽  
Tax Rate ◽  
Consistent Solution

Considering the fact that transboundary pollution control calls for the cooperation between interested parties, this paper studies a cooperative stochastic differential game of transboundary industrial pollution between two asymmetric nations in infinite-horizon level. In this paper, we model two ways of transboundary pollution: one is an accumulative global pollutant with an uncertain evolutionary dynamic and the other is a regional nonaccumulative pollutant. In our model, firms and governments are separated entities and they play a Stackelberg game, while the governments of the two nations can cooperate in pollution reduction. We discuss the feedback Nash equilibrium strategies of governments and industrial firms, and it is found that the governments being cooperative in transboundary pollution control will set a higher pollution tax rate and make more pollution abatement effort than when they are noncooperative. Additionally, a payment distribution mechanism that supports the subgame consistent solution is proposed.

Nested variational inequalities and related optimal starting–stopping problems

1992 ◽  
Vol 29 (1) ◽  
pp. 104-115 ◽  
Author(s):  
M. Sun
Keyword(s):  
Differential Equation ◽  
Variational Inequalities ◽  
Stochastic Differential Equation ◽  
Differential Game ◽  
Diffusion Model ◽  
Value Function ◽  
Optimal Strategies ◽  
Stochastic Differential Game ◽  
The Value Function

This paper introduces several versions of starting-stopping problem for the diffusion model defined in terms of a stochastic differential equation. The problem could be regarded as a stochastic differential game in which the player can only decide when to start the game and when to quit the game in order to maximize his fortune. Nested variational inequalities arise in studying such a problem, with which we are able to characterize the value function and to obtain optimal strategies.

scholarly journals Applying the noncooperative game model for compensation concept in contractor selection process for large-scale projects

2021 ◽  
Vol 15 (3) ◽  
pp. 123-135
Author(s):  
Pham Vu Hong Son ◽  
Phan Kim Anh
Keyword(s):  
Construction Projects ◽  
Large Scale ◽  
Selection Process ◽  
Noncooperative Game ◽  
Game Model ◽  
Equilibrium Solutions ◽  
Contractor Selection ◽  
Nash Equilibrium Solutions ◽  
High Effort ◽  
Proper Strategy

Nowadays, the scale of construction projects has been larger and more complex, the tender preparation is often costly to the bidder thus. It is becoming one of the primary barriers for attracting bidder’s involvement, as well as contractor's encouraging high effort. Bid compensation concept is proposed as a reward to foster the bidder participating in a higher endeavor. Game theory is ideal for modeling the dynamics and deriving high-effort strategies for bid compensation. The experiment results have demonstrated the owner can gain benefit by using rational compensation. The sensitivity analysis also shows the interest correlation between the owner and bidders. By choosing a proper strategy based on Nash Equilibrium solutions, both the owner and bidders can reach to win-win situation.  

A Differential Game Approach to Dynamic Competitive Decisions Toward Human-Computer Collaboration

2019 ◽  
Author(s):  
Alparslan Emrah Bayrak ◽  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Experimental Data ◽  
Differential Game ◽  
Equilibrium Solutions ◽  
Design Problems ◽  
High Performing ◽  
Complex Design ◽  
Nash Equilibrium Solutions ◽  
Game Theoretic ◽  
Real Time Strategy Game ◽  
Zero Sum

Abstract Partnership between humans and computers has a significant potential to extend the ability of humans to address complex design problems. This paper presents a decision-making process for computers to effectively collaborate with humans in the solution of complex problems under dynamic competition. In the proposed process, the computers learn strategies and objectives from prior experimental data and provide strategy suggestions to human collaborators. The study integrates clustering and sequential learning methods from machine learning with a differential game formulation based on model predictive control to find dynamic Nash equilibrium solutions to zero-sum games. The application of the proposed approach is demonstrated on the real-time strategy game Starcraft II that offers a dynamic competitive problem comparable in complexity to real-world applications. The results show that the proposed approach can successfully identify a variety of opening strategies in the experimental data for the initial phase of the process. The game-theoretic strategies in the later phases provide useful suggestions for low-performing players but are unnecessarily conservative for high-performing players where there is little opportunity for improvement. These results suggest a need for an assessment of the opponent expertise and a human intuition to judge the appropriateness of the game-theoretic suggestions for further improvement.

scholarly journals Optimal Network Defense Strategy Selection Method: A Stochastic Differential Game Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yan Mi ◽  
Hengwei Zhang ◽  
Hao Hu ◽  
Jinglei Tan ◽  
Jindong Wang
Keyword(s):  
Network Security ◽  
Real Time ◽  
Differential Game ◽  
Random Effects ◽  
Game Model ◽  
Random Disturbance ◽  
Stochastic Differential Game ◽  
Defense Strategy ◽  
Random Disturbances ◽  
Attack And Defense

In a real-world network confrontation process, attack and defense actions change rapidly and continuously. The network environment is complex and dynamically random. Therefore, attack and defense strategies are inevitably subject to random disturbances during their execution, and the transition of the network security state is affected accordingly. In this paper, we construct a network security state transition model by referring to the epidemic evolution process, use Gaussian noise to describe random effects during the strategy execution, and introduce a random disturbance intensity factor to describe the degree of random effects. On this basis, we establish an attack-defense stochastic differential game model, propose a saddle point equilibrium solution method, and provide an algorithm to select the optimal defense strategy. Our method achieves real-time defense decision-making in network attack-defense scenarios with random disturbances and has better real-time performance and practicality than current methods. Results of a simulation experiment show that our model and algorithm are effective and feasible.

scholarly journals Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5115 ◽  
Author(s):  
Haitao Xu ◽  
Hongjie Gao ◽  
Chengcheng Zhou ◽  
Ruifeng Duan ◽  
Xianwei Zhou
Keyword(s):  
Resource Allocation ◽  
Wireless Sensor Networks ◽  
Cognitive Radio ◽  
Energy Harvesting ◽  
Information Transmission ◽  
Wireless Sensors ◽  
Wireless Sensor ◽  
Game Model ◽  
Equilibrium Solutions ◽  
Nash Equilibrium Solutions

The progress of science and technology and the expansion of the Internet of Things make the information transmission between communication infrastructure and wireless sensors become more and more convenient. For the power-limited wireless sensors, the life time can be extended through the energy-harvesting technique. Additionally, wireless sensors can use the unauthored spectrum resource to complete certain information transmission tasks based on cognitive radio. Harvesting enough energy from the environments, the wireless sensors, works as the second users (SUs) can lease spectrum resource from the primary user (PU) to finish their task and bring additional transmission cost to themselves. To minimize the overall cost of SUs and to maximize the spectrum profit of the PU during the information transmission period, we formulated a differential game model to solve the resource allocation problem in the cognitive radio wireless sensor networks with energy harvesting, considering the SUs as the game players. By solving the proposed resource allocation game model, we found the open loop Nash equilibrium solutions and feedback Nash equilibrium solutions for all SUs as the optimal control strategies. Ultimately, series numerical simulation experiments have been made to demonstrate the rationality and effectiveness of the game model.

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