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Title: Emerging Threats and Technology Investigation: Industrial Internet of Things - Risk and Mitigation for Nuclear Infrastructure

Technical Report ·
DOI:https://doi.org/10.2172/1893157· OSTI ID:1893157
 [1];  [2];  [3];  [4];  [4];  [4];  [4];  [4];  [4]
  1. Y-12 National Security Complex, Oak Ridge, TN (United States)
  2. Avrio Analytics, Denver, CO (United States)
  3. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Industries supporting the global nuclear infrastructure striving for cost savings, expansions in efficiency, and convenience are likely to adopt components (e.g., hardware, software) that comprise the Internet of Things (IoT) and Industrial Internet of Things (IIoT). These devices offer potential improvements along with security challenges. Modern conveniences achieved through application of technology have propagated through society in the form of interconnected devices, from doorbells to microwave ovens, commonly referred to as IoT. IoT devices are often Internet-connected devices that are designed to send data back to a cloud-based server, where a smart phone application then presents device status and control options. Home-based IoT applications carry a different set of risks when compared to a business or security environment, where there is also a history of convenience and interconnection. Industrial settings have long relied on specifically designed Supervisory Control and Data Acquisition (SCADA) systems for process control where IIoT devices are intended to inform business decisions and augment traditional processes. A recent National Institute of Standards and Technology (NIST) report provides a distinction between process control and IIoT in that traditional process control is not replaced by IIoT, but rather IIoT devices are intended to enhance industrial processes through additional monitoring of various sensors and application of data analytics models using artificial intelligence (AI) and machine learning (ML) (Fagan, Marron, et al. 2021) (Ross, et al. 2021).

Research Organization:
Oak Ridge Y-12 Plant (Y-12), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
DOE Contract Number:
NA0001942
OSTI ID:
1893157
Report Number(s):
IROS65120; TRN: US2309159
Country of Publication:
United States
Language:
English