Twenty Years and Counting—Where are they? Practical Recommendations for Commercializing AI/ML for Intrusion Detection in the Nuclear Industry
- Idaho National Laboratory
Research and development into applications for improving equipment condition monitoring programs at nuclear facilities has been around since the 1990s. However, while the field has moved from using data-driven machine learning (ML) algorithms for detection and prediction of equipment degradation and failure to prognostic capabilities, these applications are still not widely used in the U.S. nuclear industry. Additionally, there has been significant effort in designing both data-driven and physics-based artificial intelligence (AI) and ML models for many other potential applications in the nuclear industry, including cyber intrusion detection systems (IDS). However, as the last twenty years in condition-based maintenance research has shown us, there are significant hurdles that must be overcome for deployment of IDS on plant systems. This paper provides a discussion on the practical recommendations that researchers should consider for successful adoption of AI/ML IDS in the nuclear industry.
- Research Organization:
- Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Sponsoring Organization:
- 51
- DOE Contract Number:
- AC07-05ID14517
- OSTI ID:
- 2278799
- Report Number(s):
- INL/CON-23-70978-Rev000
- Country of Publication:
- United States
- Language:
- English
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