Overcoming Stealthy Adversarial Attacks on Power Grid Load Predictions Through Dynamic Data Repair

Author:

Zhou Xingyu,Canady Robert,Li Yi,Koutsoukos Xenofon,Gokhale Aniruddha

Publisher

Springer International Publishing

Reference13 articles.

1. https://github.com/gridlab-d/Taxonomy_Feeders (2015). Accessed October 2019

2. Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. arXiv preprint arXiv:1802.00420 (2018)

3. Blasch, E., Bernstein, D., Rangaswamy, M.: Introduction to dynamic data driven applications systems. In: Blasch, E., Ravela, S., Aved, A. (eds.) Handbook of Dynamic Data Driven Applications Systems, pp. 1–25. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95504-9_1

4. Broll, B., Whitaker, J.: DeepForge: an open source, collaborative environment for reproducible deep learning (2017)

5. Carlini, N., Wagner, D.: Adversarial examples are not easily detected: bypassing ten detection methods. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 3–14. ACM (2017)

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