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Author Macwan, Richard, author.

Title Artificial intelligence for energy systems cybersecurity / Richard Macwan, Ryan King.

Publication Info. [Golden, Colo.] : National Renewable Energy Laboratory, 2021.

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Description 1 online resource (22 pages) : color illustrations, color map.
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series NREL/PR ; 5R00-81098
NREL/PR ; 5R00-81098.
Note "September 29, 2021."
Bibliography Includes bibliographical references.
Funding National Renewable Energy Laboratory DE-AC36-08GO28308
Note Description based on online resource, PDF version; title from cover (NREL, viewed on Sept. 22, 2022).
Summary Artificial intelligence and machine learning systems have the potential to influence the future design and implementation of cybersecurity systems for the power grid. These systems may enhance the overall operation of the power system by leveraging and making sense of massive amounts of data. However, we must also understand how AI/ML will need to be protected from cyber threat actors. We discuss the existing insights the NREL team has developed using AI/ML systems and then present resources including ESIF and the Cyber Energy Emulation Platform that can be used to generate training data and insights. We end by offering suggestions on priority research paths for AI in cybersecurity.
Subject Artificial intelligence -- Computer programs.
Computer security -- United States.
Cyberterrorism -- United States -- Prevention.
Information storage and retrieval systems -- Renewable energy sources.
Intelligence artificielle -- Logiciels.
Sécurité informatique -- États-Unis.
Systèmes d'information -- Énergies renouvelables.
Artificial intelligence -- Computer programs
Computer security
Cyberterrorism -- Prevention
Information storage and retrieval systems -- Renewable energy sources
United States https://id.oclc.org/worldcat/entity/E39PBJtxgQXMWqmjMjjwXRHgrq
Indexed Term artificial intelligence
autoencoders
cyber-energy emulation platform
cybersecurity
deep reinforcement learning
distributed denial-of-service
distribution utility emulation environment
EPRI
hybrid intrusion detection
intrusion detection systems
machine learning
research opportunities
resilience
Added Author King, Ryan, author.
National Renewable Energy Laboratory (U.S.), issuing body.
Standard No. 1825677 OSTI ID
Gpo Item No. 0430-P-09 (online)
Sudoc No. E 9.22:NREL/PR-5 R 00-81098

 
    
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