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DC Field | Value | Language |
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dc.contributor.author | Ayodeji, A | - |
dc.contributor.author | Di Buono, A | - |
dc.contributor.author | Pierce, I | - |
dc.contributor.author | Ahmed, H | - |
dc.date.accessioned | 2024-05-05T21:19:56Z | - |
dc.date.available | 2024-05-05T21:19:56Z | - |
dc.date.issued | 2024-05-03 | - |
dc.identifier | ORCiD: Abiodun Ayodeji https://orcid.org/0000-0003-3257-7616 | - |
dc.identifier | ORCiD: Iestyn Pierce https://orcid.org/0000-0003-1331-1337 | - |
dc.identifier | ORCiD: Hafiz Ahmed https://orcid.org/0000-0001-8952-4190 | - |
dc.identifier | 113277 | - |
dc.identifier.citation | Ayodeji, A. et al. (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, pp. 1 - 14. doi: 10.1016/j.nucengdes.2024.113277. | en_US |
dc.identifier.issn | 0029-5493 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28932 | - |
dc.description | Data availability: The link to data and code is embedded in the paper (available online at: https://github.com/abiodun-ayodeji/Wavy-attention-network-for-cybersecurity).. | en_US |
dc.description.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. | en_US |
dc.description.sponsorship | This work is partially supported by the High Value Manufacturing Catapult through NIN2394-FY2023 Developing Cyber Security Capability; by the CHIST-ERA funded TROCI Project (CHIST-ERA-22-SPiDDS-07) through the Engineering and Physical Sciences Research Council (EPSRC) under grant EP/Y036344/1; and by the Sêr Cymru II 80761-BU-103 project by the Welsh European Funding Office under the European Regional Development Fund. | en_US |
dc.format.extent | 1 - 14 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.uri | https://github.com/abiodun-ayodeji/Wavy-attention-network-for-cybersecurity | - |
dc.rights | Copyright © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | cybersecurity | en_US |
dc.subject | small modular reactor | en_US |
dc.subject | deep learning | en_US |
dc.subject | industrial control system | en_US |
dc.subject | intrusion detection system | en_US |
dc.subject | artificial Intelligence | en_US |
dc.title | Wavy-attention network for real-time cyber-attack detection in a small modular pressurized water reactor digital control system | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-04-29 | - |
dc.identifier.doi | https://doi.org/10.1016/j.nucengdes.2024.113277 | - |
dc.relation.isPartOf | Nuclear Engineering and Design | - |
pubs.publication-status | Published | - |
pubs.volume | 424 | - |
dc.identifier.eissn | 1872-759X | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Brunel Innovation Centre |
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FullText.pdf | Copyright © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). | 2.74 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License