Machine Learning-Based Cyberattack Detection and Identification for Automatic Generation Control Systems Considering Nonlinearities
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866910910615912448 |
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| author | Shabar, Nour M. Saber, Ahmad Mohammad Kundur, Deepa |
| author_facet | Shabar, Nour M. Saber, Ahmad Mohammad Kundur, Deepa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Based Cyberattack Detection and Identification for Automatic Generation Control Systems Considering Nonlinearities Shabar, Nour M. Saber, Ahmad Mohammad Kundur, Deepa Cryptography and Security Machine Learning 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. |
| title | Machine Learning-Based Cyberattack Detection and Identification for Automatic Generation Control Systems Considering Nonlinearities |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2504.09363 |