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Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network

In order to improve the accuracy of network security situation prediction and the convergence speed of prediction algorithm, this paper proposes a combined prediction model (EMD-ELPSO-BiGRU) based on empirical mode decomposition (EMD) and improved particle swarm optimization (ELPSO) to optimize BiGR...

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Detalles Bibliográficos
Autores principales: Zhang, Biao, Jia, Mingqi, Xu, Jiazhong, Zhao, Wanzhao, Deng, Liwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239771/
https://www.ncbi.nlm.nih.gov/pubmed/35774431
http://dx.doi.org/10.1155/2022/6031129
Descripción
Sumario:In order to improve the accuracy of network security situation prediction and the convergence speed of prediction algorithm, this paper proposes a combined prediction model (EMD-ELPSO-BiGRU) based on empirical mode decomposition (EMD) and improved particle swarm optimization (ELPSO) to optimize BiGRU neural network. Firstly, the network security situation data sequence is decomposed into a series of intrinsic mode function by EMD. Then, a particle swarm optimization algorithm (ELPSO) based on cooperative update of evolutionary state judgment and learning strategy is proposed to optimize the hyper-parameters of BiGRU neural network. Finally, a network security situation prediction model based on EMD-ELPSO-BiGRU is constructed to predict each intrinsic mode function, respectively, and the prediction results are superimposed to obtain the final network security situation prediction value. Simulation results show that ELPSO has better optimization performance, and EMD-ELPSO-BiGRU model has higher prediction accuracy and significantly improved convergence speed compared with other traditional prediction methods.