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    Recent Studies from University of New Mexico Add New Data to Engineering (Dynamic Role-based Access Control Policy for Smart Grid Applications: an Offline Deep Reinforcement Learning Approach)


    June 1, 2022 - Tech Daily News

     

      2022 MAY 31 (NewsRx) -- By a News Reporter-Staff News Editor at Tech Daily News -- Investigators publish new report on Engineering. According to news reporting from Albuquerque, New Mexico, by NewsRx journalists, research stated, “Role-based access control (RBAC) is adopted in the information and communication technology domain for authentication purposes. However, due to a very large number of entities within organizational access control (AC) systems, static RBAC management can be inefficient, costly, and can lead to cybersecurity threats.”

      Funders for this research include United States Department of Energy (DOE), United States Department of Energy (DOE), National Nuclear Security Administration.

      The news correspondents obtained a quote from the research from the University of New Mexico, “In this article, a novel hybrid RBAC model is proposed, based on the principles of offline deep reinforcement learning (RL) and Bayesian belief networks. The considered framework utilizes a fully offline RL agent, which models the behavioral history of users as a Bayesian belief-based trust indicator. Thus, the initial static RBAC policy is improved in a dynamic manner through off-policy learning while guaranteeing compliance of the internal users with the security rules of the system. By deploying our implementation within the smart grid domain and specifically within a Distributed Energy Resources (DER) ecosystem, we provide an end-to-end proof of concept of our model.”

      According to the news reporters, the research concluded: “Finally, detailed analysis and evaluation regarding the offline training phase of the RL agent are provided, while the online deployment of the hybrid RL-based RBAC model into the DER ecosystem highlights its key operation features and salient benefits over traditional RBAC models.”

      This research has been peer-reviewed.

      For more information on this research see: Dynamic Role-based Access Control Policy for Smart Grid Applications: an Offline Deep Reinforcement Learning Approach. IEEE Transactions on Human-Machine Systems, 2022. IEEE Transactions on Human-Machine Systems can be contacted at: Ieee-inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA.

      Our news journalists report that additional information may be obtained by contacting Eirini Eleni Tsiropoulou, University of New Mexico, Dept. of Electrical and Computer Engineering, Albuquerque, NM 87131, United States. Additional authors for this research include Georgios Fragkos and Jay Johnson.

      The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/THMS.2022.3163185. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

      (Our reports deliver fact-based news of research and discoveries from around the world.)

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