Adversarial attacks on deep learning models in smart grids-Reference-Cited by-同舟云学术

Adversarial attacks on deep learning models in smart grids

Published:2022-05 Issue: Volume:8 Page:123-129
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Hao Jingbo^ORCID,Tao Yang

Publisher

Elsevier BV

Subject

General Energy

Reference41 articles.

1. Smart grid — the new and improved power grid: a survey;Fang;IEEE Commun Surv Tutor,2012

2. NIST framework and roadmap for smart grid interoperability standards, release 4.0;Gopstein,2021

3. Y. Chen, Y. Tan, D. Deka, Is machine learning in power systems vulnerable? In: 2018 IEEE international conference on communications, control, and computing technologies for smart grids; 2018.

4. C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguing properties of neural networks. In: 2nd international conference on learning representations; 2014.

5. I.J. Goodfellow, J. Shlens, C. Szegedy, Explaining and harnessing adversarial examples. In: 3rd international conference on learning representations; 2015.

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A composite manifold learning approach with traditional methods for gradient-based and patch-based adversarial attack detection;Multimedia Tools and Applications;2024-07-16

2. A black-box adversarial attack on demand side management;Computers & Chemical Engineering;2024-07

3. Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review;IEEE Internet of Things Journal;2024-06-01

4. Adversarial Attacks and Defenses in Deep Learning-Based Computer Vision Systems;2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI);2024-05-24

5. Review of Regional Photovoltaic Power Dispatching Based on Artificial Intelligence;2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT);2024-04-06