中圖分類號：TM76；TP393.08 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.245197
中文引用格式： 景峰. 基于自動(dòng)編碼器和隨機(jī)樹的智能電網(wǎng)FDI檢測[J]. 電子技術(shù)應(yīng)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