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具有反应式攻击缓解的网络物理控制系统性能与韧性

Performance and Resilience of Cyber-Physical Control Systems with Reactive Attack Mitigation

University of Warwick（华威大学）；Oracle India Pvt. Ltd.（甲骨文印度私人有限公司）；GovTech Singapore（新加坡政府科技局）；Nanyang Technological University（南洋理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）

Subhash Lakshminarayana, Jabir Shabbir Karachiwala, Teo Zhan Teng, Rui Tan, David K. Y. Yau

备注：
arXiv admin note: substantial text overlap with arXiv:1706.01628
发表信息：
IEEE Trans. on Smart Grids, 2019
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AI总结：

本文研究了在电力系统中具有攻击检测和反应式攻击缓解的线性网络物理控制系统（CPCS）的性能和韧性，通过推导最优的虚假数据注入攻击序列以最大化电力系统的状态估计误差，提出基于马尔可夫决策过程（MDP）的攻击设计方法，并通过PowerWorld进行仿真验证，分析攻击检测和缓解对系统性能的影响。

摘要：

本文研究了在电力系统中具有攻击检测和反应式攻击缓解的线性网络物理控制系统（CPCS）的性能和韧性。它解决了推导最优虚假数据注入攻击序列以最大化电力系统状态估计误差的问题。结果提供了关于攻击影响限制的基本理解。攻击设计基于马尔可夫决策过程（MDP）的公式，通过价值迭代方法高效求解。我们应用所提出的框架到电力系统的电压控制系统，并使用PowerWorld进行广泛仿真。结果表明，我们的框架能够准确表征由攻击者精心设计攻击序列所引起的最大状态估计误差，因为攻击检测和缓解同时存在。此外，基于所提出的框架，我们分析了攻击检测中虚假阳性和假阴性对系统性能的影响。这些结果对于系统防御者在攻击检测和缓解的联合设计中减少这些攻击检测误差的影响至关重要。最后，由于MDP解决方案不适用于高维系统，我们应用Q学习与线性和非线性（基于神经网络）函数近似器来解决这些系统中的攻击者问题，并比较其性能。

英文摘要：

This paper studies the performance and resilience of a linear cyber-physical control system (CPCS) with attack detection and reactive attack mitigation in the context of power grids. It addresses the problem of deriving an optimal sequence of false data injection attacks that maximizes the state estimation error of the power system. The results provide basic understanding about the limit of the attack impact. The design of the optimal attack is based on a Markov decision process (MDP) formulation, which is solved efficiently using the value iteration method. We apply the proposed framework to the voltage control system of power grids and run extensive simulations using PowerWorld. The results show that our framework can accurately characterize the maximum state estimation errors caused by an attacker who carefully designs the attack sequence to strike a balance between the attack magnitude and stealthiness, due to the simultaneous presence of attack detection and mitigation. Moreover, based on the proposed framework, we analyze the impact of false positives and negatives in detecting attacks on the system performance. The results are important for the system defenders in the joint design of attack detection and mitigation to reduce the impact of these attack detection errors.Finally, as MDP solutions are not scalable for high-dimensional systems, we apply Q-learning with linear and non-linear (neural networks based) function approximators to solve the attacker's problem in these systems and compare their performances.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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