Review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics

Deployment of digital technologies within a modern shift in cyber defense systems is essential for protecting the energy production units. One of the important components of defense is cyberforensics: once an attack has been detected to locate its origin. In this paper, a review of well-kno...

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Autores principales:	Campos Brandyn M., Alamaniotis Miltiadis
Formato:	article
Lenguaje:	EN
Publicado:	VINCA Institute of Nuclear Sciences 2021
Materias:	cyberforensics digital forensics nuclear power plant internal attack neural network Nuclear and particle physics. Atomic energy. Radioactivity QC770-798
Acceso en línea:	https://doaj.org/article/7308861fc137438bba81880b707bbbda
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spelling	oai:doaj.org-article:7308861fc137438bba81880b707bbbda2021-11-22T11:03:07ZReview of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics1451-39941452-818510.2298/NTRP2102128Chttps://doaj.org/article/7308861fc137438bba81880b707bbbda2021-01-01T00:00:00Zhttp://www.doiserbia.nb.rs/img/doi/1451-3994/2021/1451-39942102128C.pdfhttps://doaj.org/toc/1451-3994https://doaj.org/toc/1452-8185Deployment of digital technologies within a modern shift in cyber defense systems is essential for protecting the energy production units. One of the important components of defense is cyberforensics: once an attack has been detected to locate its origin. In this paper, a review of well-known cyberattacks in nuclear facilities is provided, with the lessons learned leading to the development of a machine learning approach implementing identification of internal at- tacks in the facility's data networks. Our approach may be seen as one of the layers in a defense-in-depth strategy that identifies if the attack comes from inside, which may result in identifying faster the attacker's origin. The presented model exploits network packet examination to cast accurate predictions on detailing the origin of malicious network connections. The approach fuses multiple mathematical functions within an artificial neural network to provide a response in the form of 0/1, i. e., whether the attack is identified as internal or not. The utilization of a variety of test cases is developed to explore the relevance and validity of the predictive approach. The proposed implementation is examined with network data packet variance, and the results obtained exhibit a highly accurate detection rate.Campos Brandyn M.Alamaniotis MiltiadisVINCA Institute of Nuclear Sciencesarticlecyberforensicsdigital forensicsnuclear power plantinternal attackneural networkNuclear and particle physics. Atomic energy. RadioactivityQC770-798ENNuclear Technology and Radiation Protection, Vol 36, Iss 2, Pp 128-138 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cyberforensics digital forensics nuclear power plant internal attack neural network Nuclear and particle physics. Atomic energy. Radioactivity QC770-798
spellingShingle	cyberforensics digital forensics nuclear power plant internal attack neural network Nuclear and particle physics. Atomic energy. Radioactivity QC770-798 Campos Brandyn M. Alamaniotis Miltiadis Review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics
description	Deployment of digital technologies within a modern shift in cyber defense systems is essential for protecting the energy production units. One of the important components of defense is cyberforensics: once an attack has been detected to locate its origin. In this paper, a review of well-known cyberattacks in nuclear facilities is provided, with the lessons learned leading to the development of a machine learning approach implementing identification of internal at- tacks in the facility's data networks. Our approach may be seen as one of the layers in a defense-in-depth strategy that identifies if the attack comes from inside, which may result in identifying faster the attacker's origin. The presented model exploits network packet examination to cast accurate predictions on detailing the origin of malicious network connections. The approach fuses multiple mathematical functions within an artificial neural network to provide a response in the form of 0/1, i. e., whether the attack is identified as internal or not. The utilization of a variety of test cases is developed to explore the relevance and validity of the predictive approach. The proposed implementation is examined with network data packet variance, and the results obtained exhibit a highly accurate detection rate.
format	article
author	Campos Brandyn M. Alamaniotis Miltiadis
author_facet	Campos Brandyn M. Alamaniotis Miltiadis
author_sort	Campos Brandyn M.
title	Review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics
title_short	Review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics
title_full	Review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics
title_fullStr	Review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics
title_full_unstemmed	Review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics
title_sort	review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics
publisher	VINCA Institute of Nuclear Sciences
publishDate	2021
url	https://doaj.org/article/7308861fc137438bba81880b707bbbda
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Review of internal cyber attacks in nuclear facilities and an artificial neural network model for implementing internal cyberforensics

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