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: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
VINCA Institute of Nuclear Sciences
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/7308861fc137438bba81880b707bbbda |
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| Sumario: | 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. |
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