Wavy-attention network for real-time cyber-attack detection in a small modular pressurized water reactor digital control system

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

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Item Type: Article
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© 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:
  • Published: 1 August 2024
  • Published (online): 3 May 2024
  • Accepted: 29 April 2024
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
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Sustainable Development Goals:
  • Sustainable Development Goals: Goal 7: Affordable and Clean Energy
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