A GAN-Based Real-Time Covert Energy Theft Attack Against Data-Driven Detectors

Author:

Ding Zhinan,Wu Feng,Cui Lei,Hu Xiao,Xie Gang

Publisher

Springer Nature Switzerland

Reference16 articles.

1. Yao, D., Wen, M., Liang, X., et al.: Energy theft detection with energy privacy preservation in the smart grid. IEEE Internet Things J. 6(5), 7659–7669 (2019)

2. Cui, L., Guo, L., Gao, L., et al.: A covert electricity-theft cyber-attack against machine learning-based detection models. IEEE Trans. Ind. Inform. 18(11), 7824–7833 (2021)

3. Zhao, Y., Qu, Y., Xiang, Y., et al.: A comprehensive survey on edge data integrity verification: fundamentals and future trends. arXiv preprint arXiv:2210.10978 (2022)

4. Ilyas, A., Santurkar, S., Tsipras, D., et al.: Adversarial examples are not bugs, they are features. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

5. Szegedy, C., Zaremba, W., Sutskever, I., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)

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