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考虑信息困境的多类型攻击者攻防博弈模型

Attack-Defense Game Model with Multi-Type Attackers Considering Information Dilemma.

作者信息

Qi Gaoxin, Li Jichao, Xu Chi, Chen Gang, Yang Kewei

机构信息

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

出版信息

Entropy (Basel). 2022 Dec 28;25(1):57. doi: 10.3390/e25010057.

Abstract

Today, people rely heavily on infrastructure networks. Attacks on infrastructure networks can lead to significant property damage and production stagnation. The game theory provides a suitable theoretical framework for solving the problem of infrastructure protection. Existing models consider only the beneficial effects that the defender obtains from information gaps. If the attacker's countermeasures are ignored, the defender will become passive. Herein, we consider that a proficient attacker with a probability in the game can fill information gaps in the network. First, we introduce the link-hiding rule and the information dilemma. Second, based on the Bayesian static game model, we establish an attack-defense game model with multiple types of attackers. In the game model, we consider resource-consistent and different types of distributions of the attacker. Then, we introduce the solution method of our model by combining the Harsanyi transformation and the bi-matrix game. Finally, we conduct experiments using a scale-free network. The result shows that the defender can be benefited by hiding some links when facing a normal attacker or by estimating the distribution of the attacker correctly. The defender will experience a loss if it ignores the proficient attacker or misestimates the distribution.

摘要

如今,人们严重依赖基础设施网络。对基础设施网络的攻击可能导致重大财产损失和生产停滞。博弈论为解决基础设施保护问题提供了一个合适的理论框架。现有模型仅考虑防御者从信息差距中获得的有益效果。如果忽略攻击者的对策,防御者将变得被动。在此,我们认为在博弈中具有一定概率的熟练攻击者可以填补网络中的信息差距。首先,我们引入链路隐藏规则和信息困境。其次,基于贝叶斯静态博弈模型,我们建立了具有多种类型攻击者的攻防博弈模型。在博弈模型中,我们考虑攻击者资源一致和不同类型的分布情况。然后,我们通过结合哈萨尼变换和双矩阵博弈引入模型的求解方法。最后,我们使用无标度网络进行实验。结果表明,当面对普通攻击者时,防御者通过隐藏一些链路或正确估计攻击者的分布可以从中受益。如果防御者忽略熟练攻击者或错误估计分布,将会遭受损失。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8118/9858562/75b566cc889c/entropy-25-00057-g001.jpg

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