摘要： 为应对智能电网系统可能受到的新型网络攻击（如虚假数据注入攻击），提出了一种基于机器学习的入侵检测方法。该方法采用自动编码器进行数据降维，并使用极端随机树分类器检测潜在攻击。在IEEE标准电力系统数据的基础上，测试了该方法在不同系统规模和攻击程度下的性能。实验结果显示，在IEEE 118节点系统中，该方法的检测准确率高达99.76%，即使在仅有0.1%攻击测量值的情况下，F1值也达到了99.77%，远超其他算法。该方法不仅能有效检测智能电网中的入侵行为，而且具有较高的计算效率。

中图分类号：TM76；TP393.08 文献标志码：A DOI: 10.16157/j.issn.0258-7998.245197
中文引用格式： 景峰. 基于自动编码器和随机树的智能电网FDI检测[J]. 电子技术应用，2024，50(11)：80-84.
英文引用格式： Jing Feng. Smart grid FDI detection based on autoencoder and random tree[J]. Application of Electronic Technique，2024，50(11)：80-84.

Smart grid FDI detection based on autoencoder and random tree

Abstract： To cope with new types of cyber attacks (e.g. false data injection attacks) that may be applied to smart grid systems, a machine learning-based intrusion detection method is proposed. The method employs an autoencoder for data dimensionality reduction and uses an extreme random tree classifier to detect potential attacks. The performance of the method is tested under different system sizes and attack levels based on IEEE standard power system data. The experimental results show that in the IEEE 118-node system, the detection accuracy of the method is as high as 99.76%, and the F1 score reaches 99.77% even when only 0.1% of the attack measurements are available, which is much higher than other algorithms. This method is not only effective in detecting intrusions in smart grids, but also has high computational efficiency.

Key words : attack detection；autoencoder；cyber attack；extreme random tree；spurious data injection；smart grid