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Division Spotlight
Young Members Group
The Young Members Group works to encourage and enable all young professional members to be actively involved in the efforts and endeavors of the Society at all levels (Professional Divisions, ANS Governance, Local Sections, etc.) as they transition from the role of a student to the role of a professional. It sponsors non-technical workshops and meetings that provide professional development and networking opportunities for young professionals, collaborates with other Divisions and Groups in developing technical and non-technical content for topical and national meetings, encourages its members to participate in the activities of the Groups and Divisions that are closely related to their professional interests as well as in their local sections, introduces young members to the rules and governance structure of the Society, and nominates young professionals for awards and leadership opportunities available to members.
Meeting Spotlight
2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
Standards Program
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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November 2024
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Latest News
NRC restores expiration dates for renewed Turkey Point licenses
The Nuclear Regulatory Commission announced this week that it has restored the expiration dates of the Turkey Point nuclear power plant's units 3 and 4 subsequent license renewals (SLR) to July 19, 2052, and April 10, 2053, respectively.
Yeni Li, Elisa Bertino, Hany S. Abdel-Khalik
Nuclear Technology | Volume 206 | Number 1 | January 2020 | Pages 82-93
Technical Paper | 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.