Mitigation of adversarial attacks: monitoring smart grids

版本 1.0.0 (4.0 MB) 作者: BERGHOUT Tarek

These codes presents a deep learning approach based robust data engineering for mitigation of adversarial attacks and wide area monitoring.

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更新时间 2023/6/7

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These files describe an experiment performed on phasor measurement unites dataset that is made publicly available . The goal of the experiment is to train a deep network to be resilient against any adversarial attacks. A specific Robust feature engineering and a deep learning are involved in model reconstructions. fast gradient sign method and basic iterative method are involved in this case.

Notes: (i) To be able to produce experiments provided in of these codes, you have to run the "*.m" files in the directory in alphabetical order. (ii) Then you can plot results starting by any "plot...*.m" files.

link to the original paper:

https://www.researchgate.net/publication/371314420_Towards_Resilient_and_Secure_Smart_Grids_against_PMU_Adversarial_Attacks_A_Deep_Learning-Based_Robust_Data_Engineering_Approach

Please cite our work as:

Berghout, T.; Benbouzid, M.; Amirat, Y. Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach. Electronics 2023, 12, 2554. https://doi.org/10.3390/electronics12122554

引用格式

Berghout, Tarek, et al. “Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach.” Electronics, vol. 12, no. 12, MDPI AG, June 2023, p. 2554, doi:10.3390/electronics12122554.

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创建方式 R2023a

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PMU_IMAGE_DATASET

PMU_IMAGE_DATASET/ADV_Data_codes

PMU_IMAGE_DATASET/Data_processing_codes

PMU_IMAGE_DATASET/Machine_Learning_functions

版本	已发布	发行说明
1.0.0	2023/6/7		下载

MLA	Berghout, Tarek, et al. “Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach.” Electronics, vol. 12, no. 12, MDPI AG, June 2023, p. 2554, doi:10.3390/electronics12122554.
APA	Berghout, T., Benbouzid, M., & Amirat, Y. (2023). Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach. Electronics, 12(12), 2554. MDPI AG. Retrieved from https://doi.org/10.3390%2Felectronics12122554
BibTeX	@article{Berghout_2023, doi = {10.3390/electronics12122554}, url = {https://doi.org/10.3390%2Felectronics12122554}, year = 2023, month = {jun}, publisher = {{MDPI} {AG}}, volume = {12}, number = {12}, pages = {2554}, author = {Tarek Berghout and Mohamed Benbouzid and Yassine Amirat}, title = {Towards Resilient and Secure Smart Grids against {PMU} Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach}, journal = {Electronics} }