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Developing an AI-Powered Zero-Trust Cybersecurity Framework for Malware Prevention in Nuclear Power Plants

Research output: Contribution to conferencePaperpeer-review

Abstract

This study presents the development of an AI-powered Zero-Trust cybersecurity framework for malware prevention in nuclear power plants. The framework aims to enhance the security of critical systems within nuclear power plants by adopting the principles of Zero-Trust and leveraging artificial intelligence (AI) technologies. By assuming no implicit trust in any user or device and continuously authenticating and authorizing access, the framework ensures a robust defense against malware attacks. The integration of AI allows for the detection and prevention of malware through behavioral analytics, endpoint protection, network segmentation, and continuous monitoring. The paper discusses the key considerations, steps, and technologies involved in developing this framework, emphasizing the importance of regular updates, training, compliance, and auditing. The proposed framework serves as a comprehensive approach to safeguarding nuclear power plants from sophisticated malware threats and protecting the integrity and safety of critical infrastructure.
Original languageUndefined/Unknown
StatePublished - Dec 2023
Event2023 Transactions of the American Nuclear Society Winter Conference and Expo, ANS 2023 - Washington, United States
Duration: Nov 12 2023Nov 15 2023

Conference

Conference2023 Transactions of the American Nuclear Society Winter Conference and Expo, ANS 2023
Country/TerritoryUnited States
CityWashington
Period11/12/2311/15/23

INL Publication Number

  • INL/CON-23-75326
  • 164379

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