用可解释的KAN网络检测电力系统隐蔽攻击，准确率超95%。
A Kolmogorov-Arnold Network for Interpretable Cyberattack Detection in AGC Systems
- 基于柯尔莫哥洛夫-阿诺德网络建模非线性关系，实现高精度检测。
- 在真实场景下检测率达95.97%，符号公式版本达95.9%，误报率低。
- 适合关注电网安全与模型可解释性的研究人员和工程师。
自动发电控制（AGC）对电网稳定至关重要，但易受隐蔽网络攻击（如虚假数据注入攻击，FDIA）影响，此类攻击可扰乱系统稳定性却避开传统检测手段。不同于以往依赖黑箱模型的方法，本文提出柯尔莫哥洛夫-阿诺德网络（KAN）用于AGC系统的FDIA检测，充分考虑系统非线性特性。KAN模型可提取符号方程，显著提升可解释性。模型离线训练以学习不同工况下AGC测量值间的复杂非线性关系，训练后可生成描述模型行为的符号公式，极大增强可解释性。实验结果表明，所提KAN模型在初始形态下检测率达95.97%，符号公式版本达95.9%，且误报率低，为提升AGC网络安全提供了可靠方案。
原文摘要 · Abstract (English)
Automatic Generation Control (AGC) is essential for power grid stability but remains vulnerable to stealthy cyberattacks, such as False Data Injection Attacks (FDIAs), which can disturb the system's stability while evading traditional detection methods. Unlike previous works that relied on blackbox approaches, this work proposes Kolmogorov-Arnold Networks (KAN) as an interpretable and accurate method for FDIA detection in AGC systems, considering the system nonlinearities. KAN models include a method for extracting symbolic equations, and are thus able to provide more interpretability than the majority of machine learning models. The proposed KAN is trained offline to learn the complex nonlinear relationships between the AGC measurements under different operating scenarios. After training, symbolic formulas that describe the trained model's behavior can be extracted and leveraged, greatly enhancing interpretability. Our findings confirm that the proposed KAN model achieves FDIA detection rates of up to 95.97% and 95.9% for the initial model and the symbolic formula, respectively, with a low false alarm rate, offering a reliable approach to enhancing AGC cybersecurity.
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