Premium
Comparative study on different neural networks for network security situation prediction
Author(s) -
Wang Gang
Publication year - 2020
Publication title -
security and privacy
Language(s) - English
Resource type - Journals
ISSN - 2475-6725
DOI - 10.1002/spy2.138
Subject(s) - particle swarm optimization , artificial neural network , mean squared error , computer science , radial basis function , mean squared prediction error , artificial intelligence , algorithm , statistics , mathematics
This article mainly studied the performance of different neural networks in the processing network security situation prediction (NSSP). Radial basis function (RBF) and back propagation neural network (BPNN) models were optimized by particle swarm optimization (PSO) algorithm and seeker optimization algorithm (SOA), respectively. Then the PSO‐RBF model and SOA‐BPNN model were obtained, and comparative experiments were carried out on CNCERT/CC data set. The results suggested that the improved models were more accurate in predicting the situation value compared with RBF and BPNN models; the PSO‐RBF mode had three prediction errors, with 0.05 mean square error (MSE) and 0.05 mean absolute error (MAE), and the SOA‐BPNN model had six prediction errors, with 0.2 MSE and 0.13 MAE, which showed that the PSO‐RBF model had better performance. The experimental results show that the PSO‐RBF model has an excellent performance in processing NSSP and can be promoted and applied in practice.