DETECTION OF U2R ATTACKS BY MEANS OF A MULTILAYER NEURAL NETWORK
DOI:
https://doi.org/10.30888/2663-5712.2024-26-00-012Keywords:
U2R, traffic, NSL-KDD, MLNN, hyperbolic tangent function, MLNN error, error of the first kind, error of the second kindAbstract
As a research method, multi layer neural network (MLNN) configurations 41-1-Х-4 were used, where 41 is the number of input neurons; 1 – the number of hidden layers; X – the number of hidden neurons; 4 – the number of resultant neurons created using the NeMetrics
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References
NSL-KDD | Datasets | Research | Canadian Institute for Cybersecurity | UNB. University of New Brunswick | UNB. URL: https://www.unb.ca/cic/datasets/nsl.html
Pakhomova V., Kuluk V. (2022). Study of the possibility of using the RBF network to detect U2R category network attacks. ScientificWorldJournal. Bulgaria. Issue 16. Part 1. pp. 30-35. URL: https://doi.org/10.30888/2663-5712.2022-16-01-036.
Pakhomova V., Mihelbei Y. (2022). Detection of attacks of the U2R category by means of the SOM on database NSL-KDD. System Technologies. No 5(142). pp. 18-27. URL: http://eadnurt.diit.edu.ua/jspui/handle/123456789/16940.
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Published
2024-07-30
How to Cite
Пахомова, В., & Мостинець, В. (2024). DETECTION OF U2R ATTACKS BY MEANS OF A MULTILAYER NEURAL NETWORK. SWorldJournal, 1(26-01), 51–57. https://doi.org/10.30888/2663-5712.2024-26-00-012
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