Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

Abdulrahman Takiddin; Muhammad Ismail; Rachad Atat; Katherine R. Davis; Erchin Serpedin

doi:10.1109/tai.2023.3286831

journal article Mar 01, 2024

Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

Abdulrahman Takiddin

IEEE Transactions on Artificial Intelligence Vol. 5 No. 3 pp. 1287-1301 · Institute of Electrical and Electronics Engineers (IEEE)

View at Publisher Save 10.1109/tai.2023.3286831

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Abdulrahman Takiddin, Rachad Atat, Muhammad Ismail et al.

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Metrics

28

Citations

50

References

Details

Published: Mar 01, 2024
Vol/Issue: 5(3)
Pages: 1287-1301
License: View

Authors

A

Abdulrahman Takiddin

Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

M

Muhammad Ismail

Department of Computer Science, Tennessee Tech University, Cookeville, TN, USA

R

Rachad Atat

Department of Electrical and Computer Engineering Program, Texas A&M University, Qatar, Doha, Qatar

K

Katherine R. Davis

Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

E

Erchin Serpedin

Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

Funding

NSF EPCN Award: 2220346

Cite This Article

Abdulrahman Takiddin, Muhammad Ismail, Rachad Atat, et al. (2024). Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids. IEEE Transactions on Artificial Intelligence, 5(3), 1287-1301. https://doi.org/10.1109/tai.2023.3286831

Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

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