scholarly journals Research on the game of network security attack‐defense confrontation through the optimal defense strategy

2020 ◽  
Author(s):  
Fei Liu ◽  
Hongyan Gao ◽  
Zegang Wei
Keyword(s):  
Network Security ◽  
Optimal Defense ◽  
Defense Strategy ◽  
Security Attack

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 Imitative Attacker Deception in Stackelberg Security Games

2019 ◽  
Author(s):  
Thanh Nguyen ◽  
Haifeng Xu
Keyword(s):  
Recent Work ◽  
Side Effect ◽  
Learning Algorithms ◽  
Optimal Algorithms ◽  
Potential Side Effect ◽  
Optimal Defense ◽  
Defense Strategy ◽  
Game Equilibrium ◽  
Security Games ◽  
Attack Data

To address the challenge of uncertainty regarding the attacker’s payoffs, capabilities, and other characteristics, recent work in security games has focused on learning the optimal defense strategy from observed attack data. This raises a natural concern that the strategic attacker may mislead the defender by deceptively reacting to the learning algorithms. This paper focuses on understanding how such attacker deception affects the game equilibrium. We examine a basic deception strategy termed imitative deception, in which the attacker simply pretends to have a different payoff assuming his true payoff is unknown to the defender. We provide a clean characterization about the game equilibrium as well as optimal algorithms to compute the equilibrium. Our experiments illustrate significant defender loss due to imitative attacker deception, suggesting the potential side effect of learning from the attacker.

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