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English(EN) Dynamical low-rank equilibrium computation for stochastic games between advanced persistent threats and moving target defense

新方法计算网络防御博弈的纳什均衡

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为动态低秩均衡计算（DLR-NE）的新方法，用于高效计算随机博弈的纳什均衡，特别是在工业控制系统（ICS）面临高级持续性威胁（APTs）的背景下。该方法利用了ICS中攻击和防御策略固有的低秩结构，与传统的全秩值迭代相比，可以显著加快计算速度。DLR-NE保证了明确的近似误差和几何收敛性，提供了一种以更低的计算成本和经过认证的安全性来开发鲁棒防御策略的实用方法。 AI

影响 为网络安全领域复杂博弈论场景中的均衡计算引入了一种更有效的方法。

排序理由 详细介绍随机博弈新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.AI 阅读 →

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新方法计算网络防御博弈的纳什均衡

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详细介绍随机博弈新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tian Zijian, Zhang He, Chen XinJie, Liu Xinggao ·

    面向高级持续性威胁与动态目标防御之间随机博弈的动态低秩均衡计算

    arXiv:2610.06885v1 Announce Type: cross Abstract: Moving target defense (MTD) against advanced persistent threats (APTs) in industrial control systems (ICS) has well-established game-theoretic formulations, but their practical value hinges on equilibrium computation: full-rank va…

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