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. 基于自動編碼器和隨機樹的智能電網(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
Jing Feng
State Grid Corporation of Shanxi Electric Power Company Information Communication Branch
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
引言
近年來,，機器學(xué)習(xí)在檢測智能電網(wǎng)中的虛假數(shù)據(jù)注入（False Data Injection, FDI）攻擊方面發(fā)揮著越來越重要的作用[1-2]。Fadlullah等人[3]提出了基于高斯過程回歸的預(yù)測模型,，用于檢測惡意攻擊行為,。Zhang等人[4]采用支持向量機和人工免疫系統(tǒng)，設(shè)計了分布式檢測系統(tǒng),。這些傳統(tǒng)機器學(xué)習(xí)模型取得了一定成果,，但準(zhǔn)確率和魯棒性有待提高。
