Data-Driven Detection of Stealth Cyber-Attacks in DC Microgrids-Reference-Cited by-同舟云学术

Data-Driven Detection of Stealth Cyber-Attacks in DC Microgrids

Published:2022-12 Issue:4 Volume:16 Page:6097-6106
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Takiddin Abdulrahman¹^ORCID,Rath Suman²^ORCID,Ismail Muhammad³^ORCID,Sahoo Subham⁴^ORCID

Affiliation:

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

2. Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA

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

4. Department of Energy, Aalborg University, Aalborg, Denmark

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/ielx7/4267003/9979155/09818856.pdf?arnumber=9818856

Reference38 articles.

1. Deep autoencoder-based anomaly detection of electricity theft cyberattacks in smart grids;takiddin;IEEE Syst J,2022

2. ARIMA-based modeling and validation of consumption readings in power grids;krishna;Critical Information Infrastructures Security,2016

3. On Detection of False Data in Cooperative DC Microgrids—A Discordant Element Approach

4. A Distributed Finite-Time Secondary Average Voltage Regulation and Current Sharing Controller for DC Microgrids

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