Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

Journal Article · 10 November 2023 · Progress in Nuclear Energy

DOI: https://doi.org/10.1016/j.pnucene.2023.104960 · OSTI ID:2279000

Yockey, Patience  ^[1]; Erickson, Anna  ^[1]; Spirito, Christopher ^[2]

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1. Georgia Institute of Technology, Atlanta, GA (United States)
2. Idaho National Laboratory (INL), Idaho Falls, ID (United States)

We report advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States); Georgia Institute of Technology, Atlanta, GA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC07-05ID14517
• OSTI ID: 2279000
• Alternate ID(s): OSTI ID: 2369128
• Report Number(s): INL/JOU-24-76065-Revision-0; TRN: US2408146
• Journal Information: Progress in Nuclear Energy, Vol. 166, Issue -; ISSN 0149-1970
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English