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A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks

机译:智能电网系统对网络物理攻击的综合分析

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摘要

In this paper, we present a comprehensive study of smart grid security against cyber-physical attacks on its distinct functional components. We discuss: (1) a function-based methodology to evaluate smart grid resilience against cyber-physical attacks; (2) a Bayesian Attack Graph for Smart Grid (BAGS) tool to compute the likelihood of the compromise of cyber components of the smart grid system; (3) risk analysis methodology, which combines the results of the function-based methodology and BAGS to quantify risk for each cyber component of the smart grid; and (4) efficient resource allocation in the smart grid cyber domain using reinforcement learning (extension of BAGS tool) to compute optimal policies about whether to perform vulnerability assessment or patch a cyber system of the smart grid whose vulnerability has already been discovered. The results and analysis of these approaches help power engineers to identify failures in advance from one system component to another, develop robust and more resilient power systems and improve situational awareness and the response of the system to cyber-physical attacks. This work sheds light on the interdependency between the cyber domain and power grid and demonstrates that the security of both worlds requires the utmost attention. We hope this work assists power engineers to protect the grid against future cyber-physical attacks.
机译:在本文中,我们对智能电网的安全性进行了全面的研究,以应对针对其不同功能组件的网络物理攻击。我们讨论:(1)一种基于功能的方法来评估智能电网对网络物理攻击的抵御能力; (2)智能电网贝叶斯攻击图(BAGS)工具,用于计算破坏智能电网系统网络组件的可能性; (3)风险分析方法,将基于功能的方法和BAGS的结果相结合,以量化智能电网每个网络组件的风险; (4)使用强化学习(BAGS工具的扩展)在智能电网网络领域中进行有效的资源分配,以计算关于执行漏洞评估或修补已发现漏洞的智能电网网络系统的最佳策略。这些方法的结果和分析可帮助电源工程师提前从一个系统组件识别出故障,开发出功能强大且更具弹性的电源系统,并提高态势感知和系统对网络物理攻击的响应能力。这项工作揭示了网络域与电网之间的相互依赖性,并表明,这两个世界的安全都需要最大的关注。我们希望这项工作能够帮助电力工程师保护电网免受未来的网络物理攻击。

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