ML24263A255
| ML24263A255 | |
| Person / Time | |
|---|---|
| Issue date: | 09/17/2024 |
| From: | Doug Eskins Office of Nuclear Regulatory Research |
| To: | |
| References | |
| Download: ML24263A255 (1) | |
Text
Characterizing Nuclear Cybersecurity Using AI/ML NRC AI Workshop Sept. 17, 2024 Doug Eskins, NRC
Project Start: Oct 2022 Project Finish: May 2024
Project Team NRC
- Doug Eskins (nuclear, M&S*)
- Kaitlyn Cottrell (AI/ML)
- Anya Kim (cybersecurity)
Purdue University
- Prof. Stylianos Chatzidakis (Dir Nuclear Engineering Radiation Lab)
- 5 graduate students (nuclear, AI, M&S)
- Modeling and Simulation https://engineering.purdue.edu/NE/news/2023/us-nrc-visits-the-school-of-nuclear-engineering
Identify Nuclear AI-CyS Use Case Data Collection
& Model Training Performance Evaluation &
Gap Analysis Develop &
Document Insights Normal States Off Normal States Cyber Events Normal Operations Other Events Project Overview OBJECTIVES (1) Explore the use of AI/ML to classify nuclear cybersecurity states (2) Develop insights for assessment of nuclear AI-CyS
- Real data
- Algorithm performance
- Data Management Challenges and Insights Example event progression 40000 35000 30000 25000 20000 15000 10000 5000 0
0 20 60 80 Null values 40 Features Overall Null Values
- Data artifacts
- Robustness
- Explainability
Composite Classifier
Reports
- Research Plan Development (TLR-RES/DE-2024-003a), ML23040A168
- Identification of a Representative Use Case (TLR-RES/DE-2024-003b),
- Identification of AI-ML Technology (TLR-RES/DE-2024-003c),
- Use Case Implementation (TLR-RES/DE-2024-003d), ML24052A002
- Performance Evaluation and Gap Analysis (TLR-RES/DE-2024-003e),
- Final Report: Characterizing Nuclear Cybersecurity States using AI-ML (TLR-RES/DE-2024-003), ML24193A008