Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids-Reference-Cited by-同舟云学术

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

Published:2022-09 Issue:3 Volume:16 Page:4106-4117
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Takiddin Abdulrahman¹^ORCID,Ismail Muhammad²^ORCID,Zafar Usman³^ORCID,Serpedin Erchin¹^ORCID

Affiliation:

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

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

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

Funder

Qatar National Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/9866865/09674027.pdf?arnumber=9674027

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5. Automatic Assessment of Depression From Speech via a Hierarchical Attention Transfer Network and Attention Autoencoders

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