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Optimal defense strategy based on the mean field game model for cyber security

机译:基于平均场博弈模型的网络安全最优防御策略

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With the evolution of research on defense strategies in cyber security, the choice of an optimal strategy has become a key problem in current studies. Focusing on the balance between individual cost and overall network cost, we present an application of mean field game in large-scale defenders in cyber security, where players seek to construct an optimal defense strategy at their minimum cost. The contributions are threefold: first, we propose an individual cost function based on the mean field game in Hilbert space and discuss the overall network cost function, where each player has discrete-time dynamics. Then, the Nash equilibrium of the individual cost function with infinite players is researched. Finally, we establish an optimal condition in which the game equilibrium is the optimal solution to the overall cost function. Numerical examples are provided to illustrate the effectiveness of the presented strategy with an appropriate assumption.
机译:随着网络安全防御策略研究的发展,最优策略的选择已成为当前研究的关键问题。着眼于个人成本和整体网络成本之间的平衡,我们介绍了均值野外游戏在网络安全中的大型防御者中的应用,其中,玩家寻求以最低的成本构建最佳的防御策略。其贡献是三方面的:首先,我们基于希尔伯特空间中的平均场博弈提出一个单独的成本函数,并讨论整个网络成本函数,其中每个参与者都具有离散时间动态。然后,研究了具有无限参与者的个体成本函数的纳什均衡。最后,我们建立了一个最优条件,其中博弈均衡是总成本函数的最优解。提供了数值示例,以说明在适当假设下所提出策略的有效性。

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