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DRAGON: Deep Reinforcement Learning for Autonomous Grid Operation and Attack Detection

Authors:

Matthew Landen,

Jean-Paul Watson,

Wenke LeeAuthors Info & Claims

ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference

Pages 13 - 27

https://doi.org/10.1145/3564625.3567969

Published: 05 December 2022 Publication History

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Abstract

As power grids have evolved, IT has become integral to maintaining reliable power. While providing operators improved situational awareness and the ability to rapidly respond to dynamic situations, IT concurrently increases the cyberattack threat surface – as recent grid attacks such as Blackenergy and Crashoverride illustrate. To defend against such attacks, modern power grids require a system that can maintain reliable power during attacks and detect when these attacks occur to allow for a timely response. To help address limitations of prior work, we propose DRAGON– deep reinforcement learning for autonomous grid operation and attack detection, which (i) autonomously learns how to maintain reliable power operations while (ii) simultaneously detecting cyberattacks. We implement DRAGON and evaluate its effectiveness by simulating different attack scenarios on the IEEE 14 bus power transmission system model. Our experimental results show that DRAGON can maintain safe grid operations 225.5% longer than a state-of-the-art autonomous grid operator. Furthermore, on average, our detection method reports a true positive rate of 92.9% and a false positive rate of 11.4%, while also reducing the false negative rate by 63.1% compared to a recent attack detection method.

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Cited By

Ma HLv PChen KZhou J(2024)KGDist: A Prompt-Based Distillation Attack against LMs Augmented with Knowledge GraphsProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678906(480-495)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678906
Mesadieu FTorre DChennamaneni A(2024)Leveraging Deep Reinforcement Learning Technique for Intrusion Detection in SCADA InfrastructureIEEE Access10.1109/ACCESS.2024.339072212(63381-63399)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3390722
Apruzzese GLaskov PSchneider J(2023)SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP57164.2023.00042(592-614)Online publication date: Jul-2023
https://doi.org/10.1109/EuroSP57164.2023.00042

Index Terms

DRAGON: Deep Reinforcement Learning for Autonomous Grid Operation and Attack Detection
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems
  2. Software and application security
    1. Domain-specific security and privacy architectures

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ACSAC '22: Proceedings of the 38th Annual Computer Security Applications Conference

December 2022

1021 pages

ISBN:9781450397599

DOI:10.1145/3564625

Copyright © 2022 Owner/Author.

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ACSAC: Annual Computer Security Applications Conference

December 5 - 9, 2022

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Ma HLv PChen KZhou J(2024)KGDist: A Prompt-Based Distillation Attack against LMs Augmented with Knowledge GraphsProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678906(480-495)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678906
Mesadieu FTorre DChennamaneni A(2024)Leveraging Deep Reinforcement Learning Technique for Intrusion Detection in SCADA InfrastructureIEEE Access10.1109/ACCESS.2024.339072212(63381-63399)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3390722
Apruzzese GLaskov PSchneider J(2023)SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP57164.2023.00042(592-614)Online publication date: Jul-2023
https://doi.org/10.1109/EuroSP57164.2023.00042

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