Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids

Abdulrahman Takiddin; Muhammad Ismail; Usman Zafar; Erchin Serpedin

doi:10.1109/jsyst.2021.3136683

journal article Sep 01, 2022

Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids

Abdulrahman Takiddin

Muhammad Ismail

Usman Zafar

Erchin Serpedin

IEEE Systems Journal Vol. 16 No. 3 pp. 4106-4117 · Institute of Electrical and Electronics Engineers (IEEE)

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Citations

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References

Details

Published: Sep 01, 2022
Vol/Issue: 16(3)
Pages: 4106-4117
License: View

Authors

A

Abdulrahman Takiddin

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

U

Usman Zafar

Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, Qatar

E

Erchin Serpedin

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

Funding

Qatar National Research Fund Award: NPRP12S-0221-190127

Cite This Article

Abdulrahman Takiddin, Muhammad Ismail, Usman Zafar, et al. (2022). Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids. IEEE Systems Journal, 16(3), 4106-4117. https://doi.org/10.1109/jsyst.2021.3136683

Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids

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