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Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
2020 ANS Virtual Winter Meeting
November 16–19, 2020
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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U.S. reactor technologies to be featured at IAEA conference
A virtual side event at the 64th General Conference of the International Atomic Energy Agency will spotlight U.S. reactor technologies. The free event, US Reactor Technologies: Flexible Energy Security for Real-World Challenges, will be held this Thursday, September 24, from 9:00 a.m. to 10:30 a.m. (EDT).
The event will highlight the capabilities of small modular reactors and other innovative reactors for addressing countries’ current needs. It will also examine anticipated challenges in the future, as well as underscore the need to act now.
The event is sponsored by the U.S. Department of Energy’s Office of Nuclear Energy. Advanced registration is required.
Yeni Li, Elisa Bertino, Hany S. Abdel-Khalik
Nuclear Technology | Volume 206 | Number 1 | January 2020 | Pages 82-93
Technical Paper | dx.doi.org/10.1080/00295450.2019.1626170
Articles are hosted by Taylor and Francis Online.
Model-based defenses have been promoted over the past decade as essential defenses against intrusion and data deception attacks into the control network used to digitally regulate the operation of critical industrial systems such as nuclear reactors. The idea is that physics-based models could differentiate between genuine, i.e., unaltered by adversaries, and malicious network engineering data, e.g., flowrates, temperatures, etc. Machine learning techniques have also been proposed to further improve the differentiating power of model-based defenses by constantly monitoring the engineering data for any possible deviations that are not consistent with the physics. While this is a sound premise, critical systems, such as nuclear reactors, chemical plants, oil and gas plants, etc., share a common disadvantage: almost any information about them can be obtained by determined adversaries, such as state-sponsored attackers. Thus, one must question whether model-based defenses would be resilient under these extreme adversarial conditions. This paper represents a first step toward answering this question. Specifically, we introduce self-learning techniques, including both pure data-driven, e.g., deep neural networks, and physics-based techniques able to predict dynamic behavior for a nuclear reactor model. The results indicate that if attackers are technically capable, they can learn very accurate models for reactor behavior, which raises concerns about the effectiveness of model-based defenses.