Ayodeji, A. orcid.org/0000-0003-3257-7616, Di Buono, A., Pierce, I. orcid.org/0000-0003-1331-1337 et al. (1 more author) (2024) Wavy-attention network for real-time cyber-attack detection in a small modular pressurized water reactor digital control system. Nuclear Engineering and Design, 424. 113277. ISSN 0029-5493
Abstract
Global interest in advanced reactors has been reignited by recent investments in small modular reactors and micro-reactor design. The use of digital devices is essential for meeting the size and modularity requirements of small modular reactor controls. By fully digitizing the small modular reactor control systems, critical information can be obtained to optimize control, reduce costs, and extend the reactor's lifetime. However, the potential for cyber-attacks on digital devices leaves digital control systems vulnerable. To address this risk, this study presents a novel wavy-attention network for sensor attack detection in nuclear plants. The wavy-attention network comprises stacks of batch-normalized, dilated, one-dimensional convolution neural networks, and sequential self-attention modules, superior to conventional single-layer networks on sequence classification tasks. To evaluate the proposed wavy-attention network architecture, the International Atomic Energy Agency's Asherah Nuclear Simulator and a false data injection toolbox found in the literature, both implemented in MATLAB/SIMULINK, are utilized. This approach leverages changes in process measurements to identify and classify cyber-attacks on priority signals using the proposed wavy-attention network. Three false data injection attacks are simulated on the simulator's pressure, temperature, and level sensors to obtain representative process measurements. The wavy-attention network is trained and validated with normal and compromised process variables obtained from the simulator. The performance of the wavy-attention network to discriminate between the reactor states using the test set shows 99% accuracy, as opposed to other baseline models such as vanilla convolution neural networks, long short-term memory networks, and bi-directional long short-term memory networks with 90%, 77%, and 91% accuracy, respectively. An ablation study is also conducted to test the contribution of each component of the proposed architecture. The theoretical framework of the proposed wavy-attention network and its implementation for nuclear reactor digital sensor attack detection are discussed in this paper.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Cybersecurity; Small modular reactor; Deep learning; Industrial control system; Intrusion detection system; Artificial Intelligence |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > Nuclear Advanced Manufacturing Research Centre |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Jul 2024 13:44 |
Last Modified: | 10 Jul 2024 13:56 |
Published Version: | http://dx.doi.org/10.1016/j.nucengdes.2024.113277 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.nucengdes.2024.113277 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214681 |