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Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

机译：智能电网中稀疏的恶意虚假数据注入攻击与防御机制

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摘要

This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.
机译：本文讨论了对智能电网中的广域测量和监视系统的恶意虚假数据注入攻击。首先，针对两种典型情况开发了构造稀疏隐身攻击的方法：1）随机攻击，其中任意测量都可能受到损害; 2）针对性攻击，其中指定的状态变量被修改。已经证明，如果被破坏的测量数量超过某个值，则隐身攻击将始终存在。在本文中，发现仅通过修改比该值少得多的测量值就可以完成随机的不可检测的攻击。众所周知，可以通过使某些测量子集不受攻击来保护系统免受恶意攻击。然后提出一种有效的贪婪搜索算法，以快速找到要保护的测量子集以防御隐形攻击。结果表明，该贪婪算法的性能与蛮力方法几乎相同，但没有组合复杂性。第三，讨论了一种鲁棒的攻击检测方法。通过引入基于元素的约束，基于鲁棒主成分分析问题设计了检测方法。事实表明，即使仅收集部分观测值，该方法也能够识别实际测量值和攻击。仿真是基于IEEE测试系统进行的。

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