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Division Spotlight
Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
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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Latest News
Tank waste operations resume at Idaho’s IWTU
The Department of Energy’s Office of Environmental Management announced yesterday that waste processing operations have resumed at the Integrated Waste Treatment Unit (IWTU) at the Idaho National Laboratory Site. The resumption of operations follows the completion of two maintenance campaigns at the radioactive liquid waste treatment facility.
Fan Zhang, J. Wesley Hines, Jamie Coble (Univ of Tennessee, Knoxville)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 1370-1379
The industry targeted cyber-attacks has increased in the recent years. In addition, digitalizing the instrumentation and control brings new cybersecurity challenges to the supervisory control and data acquisition (SCADA) system; therefore, the cybersecurity of the SCADA system should be enhanced. This paper proposed an auto-associative kernel regression (AAKR) data-driven model based on network flow data for early attack detection of SCADA system. This model is integrated to the cyber-attack detection system (CADS) proposed before to provide multi-layer defense and better coverage of detectable cyber-attacks. For generating data for the research, a real-time SCADA system test bed was built at the University of Tennessee. It contains a physically modeled thermal-hydraulic part for a typical two-loop nuclear system; a SCADA system based on LabVIEW and a programmable logic controller (PLC); a local area network (LAN)-based cyber network which enables cyber-attacks and data collection. The normal data set and two abnormal data sets fed into the proposed auto-associative kernel regression (AAKR) model were collected under the normal operation, man in the middle (MITM) and denial of service (DoS) attack scenarios, respectively. The result shows that the proposed AAKR model is able to detect the MITM and DoS attacks effectively with almost no false alarms.