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Joint estimation-detection of cyber attacks in smart grids: Bayesian and non-Bayesian formulations

机译：智能电网中网络攻击的联合估计检测：贝叶斯和非贝叶斯公式

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摘要

Smart grid operations face a significant threat from the presence of cyber attacks or bad data that may contaminate the system observations. Therefore, in this paper, we are interested in introducing a new strategy for detecting the presence of bad data in smart grids and we also try to simultaneously estimate it in order to be able to separate the bad data from the system observations. We aim to obtain the attack free observations which reflect the true state of the smart grid. This can be done by defining a joint detection-estimation strategy based on Bayesian and non-Bayesian settings where the costs in general will be functions of the observation. We start with Bayes approach and derive the detector (which, in general, may not be a LRT) and then we set the problem by defining some maximum constraint under the null hypothesis based on the derived detector and minimize certain cost under the alternative hypothesis. Our results reveal that the proposed model is applicable on some cases that other models reported in previous works failed to deal with.
机译：智能电网运营面临着可能会污染系统观察结果的网络攻击或不良数据的严重威胁。因此，在本文中，我们有兴趣介绍一种新的策略来检测智能电网中不良数据的存在，并且我们还尝试同时对其进行估计，以便能够将不良数据与系统观测值分开。我们旨在获得反映智能电网真实状态的无攻击观察结果。这可以通过定义基于贝叶斯和非贝叶斯设置的联合检测估计策略来完成，其中成本通常是观测的函数。我们从贝叶斯方法开始，推导检测器（通常可能不是LRT），然后通过基于派生的检测器在零假设下定义一些最大约束，并在替代假设下最小化某些成本来设置问题。我们的结果表明，所提出的模型适用于某些先前案例中报道的其他模型未能处理的情况。

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