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Telecommunications and Radio Engineering

Выходит 12 номеров в год

ISSN Печать: 0040-2508

ISSN Онлайн: 1943-6009

SJR: 0.185 SNIP: 0.268 CiteScore™:: 1.5 H-Index: 22

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A COMPUTER NETWORK INTRUSION DETECTION TECHNOLOGY BASED ON IMPROVED NEURAL NETWORK ALGORITHM

Том 79, Выпуск 7, 2020, pp. 593-601
DOI: 10.1615/TelecomRadEng.v79.i7.50
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Краткое описание

In order to resist the network malicious attack, Back-Propagation (BP) neural network was improved by particle swarm optimization (PSO) algorithm. Then the simulation analysis was carried out in the MATLAB software. The results showed that the improved BP algorithm converged faster and the error was smaller when training the algorithm; compared with BP, PSO-BP had higher accuracy and precision and lower false positive rate, and it also had better detection performance when the size of training samples was small. In summary, PSO-BP can be used for the detection of network intrusion threats.

ЛИТЕРАТУРА
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ЦИТИРОВАНО В
  1. Dubey Ghanshyam Prasad, Investigating the Impact of Feature Reduction Through Information Gain and Correlation on the Performance of Error Back Propagation Based IDS, International Journal of Electrical and Electronics Research, 9, 3, 2021. Crossref

  2. Dubey Ghanshyam Prasad, Bhujade Rakesh Kumar, Himthani Puneet, Significance of Dimensionality Reduction in Intrusion Detection Dataset, in Proceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021), 15, 2022. Crossref

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