Machine Learning-Based Cyberattack Detection and Identification for Automatic Generation Control Systems Considering Nonlinearities
This addresses security vulnerabilities in power grid control systems, but it is incremental as it applies known ML algorithms to a specific domain problem.
The paper tackles the problem of false data injection attacks on automatic generation control systems in power grids by proposing a machine learning-based detection framework, achieving an F1-score of up to 99.98% and outperforming existing methods.
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.