Abstract Smart grid is a crucial Cyber-Physical system and is prone to False Data Injection Attack (FDIA). In this paper, we propose a novel detection mechanism for a new-type FDIA which targets at inducing generation rescheduling and load shedding. We exploit a signal processing method to recognize the behavior features of the estimated states under this FDIA and employ the captured features to train a time-series-analysis based detector. Before training the detector, an improved ELM method is proposed to eliminate the redundancies of the feature vectors. By doing so, our proposed detection mechanism can effectively detect the new-type FDIA by analysing the deviations between the feature vectors in both the spatial and temporal aspects. We assess the performance of the proposed mechanism with comprehensive simulations on IEEE 14- and 118-bus systems. The results indicate that the proposed mechanism can be performed in a real-time way with satisfactory detection accuracy.

中文翻译：

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基于时间序列分析方法的智能电网双层虚假数据注入攻击检测

摘要 智能电网是一个重要的信息物理系统，容易受到虚假数据注入攻击（FDIA）。在本文中，我们提出了一种新型 FDIA 的新型检测机制，其目标是诱导发电重新调度和减载。我们利用信号处理方法来识别此 FDIA 下估计状态的行为特征，并利用捕获的特征来训练基于时间序列分析的检测器。在训练检测器之前，提出了一种改进的 ELM 方法来消除特征向量的冗余。通过这样做，我们提出的检测机制可以通过分析特征向量在空间和时间方面的偏差来有效地检测新型 FDIA。我们通过对 IEEE 14 和 118 总线系统的综合模拟来评估所提出机制的性能。结果表明，所提出的机制可以实时执行，并具有令人满意的检测精度。