@misc{96715580f4444ba3adad028a7ed8c50e,
title = "Artificial Intelligence for Energy Systems Cybersecurity",
abstract = "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.",
keywords = "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",
author = "Richard Macwan and Ryan King",
year = "2021",
language = "American English",
series = "Presented at the Artificial Intelligence Summit Cyber Security Grand Challenge, 29 September 2021",
type = "Other",
}