scholarly journals Attack-Defense Differential Game Model for Network Defense Strategy Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 50618-50629 ◽  
Author(s):  
Hengwei Zhang ◽  
Lv Jiang ◽  
Shirui Huang ◽  
Jindong Wang ◽  
Yuchen Zhang
Keyword(s):  
Differential Game ◽  
Game Model ◽  
Strategy Selection ◽  
Defense Strategy ◽  
Network Defense

scholarly journals Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization

2017 ◽  
Vol 7 (11) ◽  
pp. 1138 ◽  
Author(s):  
Yang Sun ◽  
Wei Xiong ◽  
Zhonghua Yao ◽  
Krishna Moniz ◽  
Ahmed Zahir
Keyword(s):  
Reinforcement Learning ◽  
Pareto Optimization ◽  
Strategy Selection ◽  
Defense Strategy ◽  
Network Defense

scholarly journals Markov Evolutionary Games for Network Defense Strategy Selection

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 19505-19516 ◽  
Author(s):  
Jianming Huang ◽  
Hengwei Zhang ◽  
Jindong Wang
Keyword(s):  
Evolutionary Games ◽  
Strategy Selection ◽  
Defense Strategy ◽  
Network Defense

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.

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