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对抗性强化学习发现稀疏拒绝服务攻击以破坏自触发控制系统

研究人员开发了一种新颖的对抗性强化学习方法,用于识别自触发控制系统中的漏洞。该方法侧重于寻找最稀疏的拒绝服务(DoS)攻击计划,这些计划能够破坏系统,类似于防御者使用的安全证书。该研究证明了对手造成崩溃所需的干扰次数的下限,并在 PendulumCartPoleQuadrotor2D 等模拟环境中,通过实证证明了所学习到的对手对各种控制器都有效。 AI

影响 这项研究通过识别和缓解新的攻击向量,有望使控制系统更加健壮。

排序理由 该集群包含一篇研究论文,详细介绍了用于分析控制系统漏洞的新颖对抗性强化学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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对抗性强化学习发现稀疏拒绝服务攻击以破坏自触发控制系统

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该集群包含一篇研究论文,详细介绍了用于分析控制系统漏洞的新颖对抗性强化学习方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Adam Haroon, Erick J. Rodr\'iguez-Seda, Tristan Schuler, Cody Fleming ·

    逆转自触发控制:用于稀疏拒绝服务攻击的对抗性强化学习

    arXiv:2609.12016v1 Announce Type: new Abstract: Self-triggered reinforcement learning control (RL-STC) learns the sparsest control schedule that preserves Lyapunov-decreasing stability under a Run-Time Assurance (RTA) override. We invert this: an adversarial RL agent learns the s…