用机器学习检测电力系统中虚假数据攻击，准确率超99%。
Machine Learning-Based Cyberattack Detection and Identification for Automatic Generation Control Systems Considering Nonlinearities
- 基于统计与时间序列特征训练模型，实时识别被篡改数据
- 在扰动前后数据上测试，F1分数达99.98%，误报率低
- 适合电网安全防护人员和电力系统研究者参考
自动发电控制（AGC）系统在维持电力系统频率稳定中起关键作用。然而，其依赖通信测量数据的特性使其易受虚假数据注入攻击（FDIA）影响，可能破坏系统整体稳定性。本文提出一种基于机器学习（ML）的检测框架，可识别FDIA并定位被攻击的测量信号。该方法利用离线训练的ML模型，基于扰动前后的AGC测量数据提取统计与时间序列特征进行攻击检测与信号分类。对比多种先进机器学习算法性能，结果表明所提方法在检测FDIA方面效果显著，F1分数最高达99.98%，优于现有方法，同时保持低误报率。
原文摘要 · Abstract (English)
Automatic generation control (AGC) systems play a crucial role in maintaining system frequency across power grids. However, AGC systems' reliance on communicated measurements exposes them to false data injection attacks (FDIAs), which can compromise the overall system stability. This paper proposes a machine learning (ML)-based detection framework that identifies FDIAs and determines the compromised measurements. The approach utilizes an ML model trained offline to accurately detect attacks and classify the manipulated signals based on a comprehensive set of statistical and time-series features extracted from AGC measurements before and after disturbances. For the proposed approach, we compare the performance of several powerful ML algorithms. Our results demonstrate the efficacy of the proposed method in detecting FDIAs while maintaining a low false alarm rate, with an F1-score of up to 99.98%, outperforming existing approaches.
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