A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 11
Issue 3
IEEE/CAA Journal of Automatica Sinica
| Citation: | C. Ren, C. Zou, Z. Xiong, H. Yu, Z.-Y. Dong, and N. Dusit, “Achieving 500X acceleration for adversarial robustness verification of tree-based smart grid dynamic security assessment,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 800–802, Mar. 2024. doi: 10.1109/JAS.2023.124053
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| [1] |
L. Duchesne, E. Karangelos, L. Wehenkel, et al., “Recent developments in machine learning for energy systems reliability management,” Proc. IEEE, vol. 108, no. 9, pp. 1656–1676, 2020.
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| [2] |
Y. Wang, Q. Chen, T. Hong, and C. Kang, “Review of smart meter data analytics: Applications methodologies, and challenges,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3125–3148, May 2019. doi: 10.1109/TSG.2018.2818167
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| [3] |
X. Yuan, P. He, Q. Zhu, et al., “Adversarial examples: Attacks and defenses for deep learning,” IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2805–2924, 2019.
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| [4] |
C. Ren, X. Du, Y. Xu, Q. Song, Y. Liu, and R. Tan, “Vulnerability analysisrobustness verification, and mitigation strategy for machine learning-based power system stability assessment model under adversarial examples,” IEEE Trans. Smart Grid, vol. 13, no. 2, pp. 1622–1632, Mar. 2022. doi: 10.1109/TSG.2021.3133604
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| [5] |
Z. Zhang and D. K. Y. Yau, “CoRE: Constrained robustness evaluation of machine learning-based stability assessment for power systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 557–559, 2023. doi: 10.1109/JAS.2023.123252
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| [6] |
C. Ren and Y. Xu, “A universal defense strategy for data-driven power system stability assessment models under adversarial examples,” IEEE Internet of Things J., vol. 10, no. 9, pp. 7568–7576, May 2023. doi: 10.1109/JIOT.2022.3202267
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| [7] |
M. Andriushchenko and M. Hein, “Provably robust boosted decision stumps and trees against adversarial attacks,” in Proc. Advances in Neural Inform. Processing Systems, 2019, pp. 12997–13008.
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| [8] |
Y. Wang, H. Zhang, H. Chen, et al., “On LP-norm robustness of ensemble decision stumps and trees,” in Proc. Int. Conf. Machine Learning, 2020, pp. 10104–10114.
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| [9] |
H. Chen, H. Zhang, S. Si, Y. Li, D. Boning, and C.-J. Hsieh, “Robustness verification of tree-based models,” in Proc. Advances in Neural Information Processing Systems, 2019, pp. 12317–12328.
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