Optimal Network Defense Strategy Selection Based on Incomplete Information Evolutionary Game

Hu, Hao; Liu, Yuling; Zhang, Hongqi; Pan, R.

doi:10.1109/access.2018.2841885

Cited by 37 publications

(41 citation statements)

References 17 publications

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“…e most important thing is the introduction of WoLF mechanism, which guarantees the convergence of the algorithm [9]. WoLF-PHC algorithm uses average strategy as the criterion of success and failure, as shown in formulae (6) and (7).…”

Section: Phc Algorithmmentioning

confidence: 99%

“…(1) N � (attacker, defender) are players participating in the game representing cyber attackers and defenders, respectively Web server CVE-2017-7269 root tid 3 Web server CVE-2014-8517 root tid 4 FTP server CVE-2014-3556 root tid 5 E-mail server CVE-2014-4877 root tid 6 Database server CVE-2013-4730 user tid 7 Database server CVE-2016-6662 root S 0 (A, root) [16,26] are S 6 (D, root) Figure 8: Defense graph. (5) In order to detect the learning performance of the Algorithm 2 more fully, the defender's state-action payoff Q d (s i , d) is initialized with a unified 0, without introducing additional prior knowledge (6) Defender's defense strategy adopts average strategy to initialize, that is,…”

Section: Construction Of the Experiments Scenario Ad-sgmmentioning

confidence: 99%

“…It not only extends the single state of traditional game to multistate but also characterizes the randomness of cyber attack-defense. At present, the cyber security analysis based on stochastic game has achieved some results, but there are still some shortcomings and challenges [4][5][6][7]. e existing stochastic game of attack-defense is based on the assumption of complete rationality, through Nash equilibrium for attack prediction and defense guidance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

Zhang

Liu

2019

Security and Communication Networks

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Existing approaches of cyber attack-defense analysis based on stochastic game adopts the assumption of complete rationality, but in the actual cyber attack-defense, it is difficult for both sides of attacker and defender to meet the high requirement of complete rationality. For this aim, the influence of bounded rationality on attack-defense stochastic game is analyzed. We construct a stochastic game model. Aiming at the problem of state explosion when the number of network nodes increases, we design the attack-defense graph to compress the state space and extract network states and defense strategies. On this basis, the intelligent learning algorithm WoLF-PHC is introduced to carry out strategy learning and improvement. Then, the defense decision-making algorithm with online learning ability is designed, which helps to select the optimal defense strategy with the maximum payoff from the candidate strategy set. The obtained strategy is superior to previous evolutionary equilibrium strategy because it does not rely on prior data. By introducing eligibility trace to improve WoLF-PHC, the learning speed is further improved and the defense timeliness is significantly promoted.

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Section: Phc Algorithmmentioning

confidence: 99%

Section: Construction Of the Experiments Scenario Ad-sgmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

Zhang

Liu

2019

Security and Communication Networks

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“…Therefore, the attack-defense effect can be measured by the value of network system. Based on the above analysis of the realistic network attack and defense [8] [17] ， the establishment of the attack-defense game model needs to satisfy three assumptions as follows: Assumption 1. Rational assumption.…”

Section: Basic Assumptionsmentioning

confidence: 99%

Optimal Network Defense Strategy Selection Based on Markov Bayesian Game

2019

KSII TIIS

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The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

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“…Huang [11] built a network attack and defense model by combining evolutionary game theory and the Markov decision process, and proposed a method for selecting the optimal defense strategy. Hu [12] expanded the static analysis of the attack and defense process into dynamic analysis under the condition of incomplete information, using the Bayesian method to obtain the optimal defense strategy. Olsder [13] formulated a Stackelberg game in the case of a nonzero-sum game and concluded that if each player's cost function is either nonquadratic or nonconvex, both pure and mixed Stackelberg strategies can achieve equilibrium.…”

Section: Introductionmentioning

confidence: 99%

MAIAD: A Multistage Asymmetric Information Attack and Defense Model Based on Evolutionary Game Theory

Che

Zeng

et al. 2019

Symmetry

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With the rapid development and widespread applications of Internet of Things (IoT) systems, the corresponding security issues are getting more and more serious. This paper proposes a multistage asymmetric information attack and defense model (MAIAD) for IoT systems. Under the premise of asymmetric information, MAIAD extends the single-stage game model with dynamic and evolutionary game theory. By quantifying the benefits for both the attack and defense, MAIAD can determine the optimal defense strategy for IoT systems. Simulation results show that the model can select the optimal security defense strategy for various IoT systems.

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Optimal Network Defense Strategy Selection Based on Incomplete Information Evolutionary Game

Cited by 37 publications

References 17 publications

Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

Optimal Network Defense Strategy Selection Based on Markov Bayesian Game

MAIAD: A Multistage Asymmetric Information Attack and Defense Model Based on Evolutionary Game Theory

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