Research on Internet Security Situation Awareness Prediction Technology Based on Improved RBF Neural Network Algorithm

Authors

  • Zhihua Chen Guangdong Polytechnic Normal University, China

DOI:

https://doi.org/10.47852/bonviewJCCE149145205514

Keywords:

RBF neural network, network security, situation awareness, prediction, application

Abstract

With the increasing scale and complexity of the network, the network attack technology is also changing, such as malicious
program attack, Trojan horse, distributed denial of service attack, worm, virus, web code injection, botnet, and other new network attack tools emerge in large numbers. As the core hotspot of network information security, network security situational awareness has received more and more attention. The traditional way of network security situational awareness prediction is relatively single. Usually, only one algorithm is used for perception and prediction, and its prediction accuracy is limited. To explore the application effect of intelligent learning algorithm, this study takes radial basis function (RBF) neural network as the main research object, optimizes RBF by simulated annealing (SA) algorithm and hybrid hierarchy genetic algorithm (HHGA), constructs RBF neural network prediction model based on SA–HHGA optimization, and carries out relevant experiments. The results show that the predicted situation value of the optimized RBF neural network in 15 samples is very close to the actual situation value. The neural network has good prediction effect and can provide assistance for the maintenance of network security.

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Published

2022-03-25

How to Cite

Chen, Z. (2022). Research on Internet Security Situation Awareness Prediction Technology Based on Improved RBF Neural Network Algorithm. Journal of Computational and Cognitive Engineering, 1(3), 103–108. https://doi.org/10.47852/bonviewJCCE149145205514