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Detection of false data injection attacks in smart grid under colored Gaussian noise

机译：高斯噪声下智能电网中虚假数据注入攻击的检测

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In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate the state of the power transmission networks and develop a generalized likelihood ratio test (GLRT) detector for the detection of false data injection attacks. We show that the conventional approach with the assumption of Gaussian noise is a special case of the proposed method, and thus the new approach has more applicability. The proposed detector is also tested on an independent component analysis (ICA) based unobservable false data attack scheme that utilizes similar assumptions of sample observation. We evaluate the performance of the proposed state estimator and attack detector on the IEEE 30-bus power system with comparison to conventional Gaussian noise based detector. The superior performance of both observable and unobservable false data attacks demonstrates the effectiveness of the proposed approach and indicates a wide application on the power signal processing.
机译：在本文中，我们考虑了当测量被彩色高斯噪声破坏时，智能电网中的状态估计和错误数据注入检测的问题。通过使用自回归过程对噪声建模，我们估计了电力传输网络的状态，并开发了一种通用似然比测试（GLRT）检测器，用于检测错误的数据注入攻击。我们表明，在假设高斯噪声的情况下，传统方法是该方法的特例，因此，新方法具有更大的适用性。所提出的检测器还在基于独立分量分析（ICA）的不可观察的虚假数据攻击方案上进行了测试，该方案利用了样本观察的类似假设。与传统的基于高斯噪声的检测器相比，我们评估了在IEEE 30总线电源系统上所提出的状态估计器和攻击检测器的性能。可观察和不可观察的错误数据攻击的优越性能证明了所提出方法的有效性，并表明了在功率信号处理方面的广泛应用。

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