A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders
Stealthy FDIA (False Data Injection Attack) is a serious cyber threat that can modify state estimation of smart grid through maliciously altering the measurement data, but can’t be detected by traditional bad data detection system in smart grid. There exist two weakpoints for numerous deep neural ne...
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| Veröffentlicht in: | Applied soft computing Jg. 151; S. 111169 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.01.2024
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| Schlagworte: | |
| ISSN: | 1568-4946, 1872-9681 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Stealthy FDIA (False Data Injection Attack) is a serious cyber threat that can modify state estimation of smart grid through maliciously altering the measurement data, but can’t be detected by traditional bad data detection system in smart grid. There exist two weakpoints for numerous deep neural networks (DNNs) based data-driven schemes against FDIA. First, they mainly focus on detecting the presence of FDIA, but fail to localize the specific bus/nodes affected. Second, their performance is not sufficiently desirable under small attack, i.e., anomalies caused by the attack closely resemble normal data. To address the above issues, this paper proposes an effective locational FDIA detection framework based on data reconstruction, AT-GVAE, which seamlessly integrates the variational autoencoders (VAEs) and generative adversarial network paradigm. Specifically, our contributions are threefold. First, the proposed AT-GVAE framework is novelly composed of two main modules: the generative VAE_G and discriminative VAE_D that both play dual roles: reconstruct data from jointly learning distributions of data and latent feature space, and meanwhile play Minmax game with adversarial way. Second, multiple-layer gated recurrent units (GRUs) are utilized as the basic structure of the encoder and decoders in both VAEs, to characterize the temporal correlations of measurement data sequence. Additionally, the self-attention mechanism is used to enhance the expressive ability of GRU based VAEs. Then, the anomaly score for each busbar in the smart grid is determined by comparing the residual between the observed measurement and the outputs of VAE_G and VAE_D, enabling the localization of FDIA. Finally, thorough experiments on multiple power systems with series data of total 3456 measurements demonstrate that, in terms of multiple typical metrics, including false alarm rate vs. detection probability, AUC-ROC, and AUC-PR, our proposed framework outperforms the state-of-the-art GRU, VAE and adversarial training based FDIA detection schemes.
•Propose a novel locational FDIA detection framework, seamlessly integrating the VAEs and GAN paradigm.•The generative and discriminative VAEs both have dual roles: reconstruct data and play minmax game with adversarial way.•Multiple-layer gated recurrent units are utilized as the basic structure of the encoder and decoders in both VAEs.•Demonstrate our proposal outperforms SOTA VAE and adversarial training based FDIA schemes on multiple power systems. |
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| ISSN: | 1568-4946 1872-9681 |
| DOI: | 10.1016/j.asoc.2023.111169 |