TY - JOUR
T1 - Network traffic prediction based on least squares support vector machine with simple estimation of Gaussian kernel width
AU - Ke, Gang
AU - Chen, Ruey Shun
AU - Ji, Shanshan
AU - Yeh, Jyh Haw
N1 - Publisher Copyright:
Copyright © 2022 Inderscience Enterprises Ltd.
PY - 2022
Y1 - 2022
N2 - In order to improve the accuracy of network traffic prediction and overcome the disadvantages of slow convergence speed and easy to fall into local minimum value in the process of least squares support vector machine (LSSVM) network traffic prediction, a network traffic security prediction model based on LSSVM which simply estimates the width of Gaussian kernel is proposed. The model assigns different Gauss kernel widths for each sampling point according to the local density of the sampling point. The simulation results show that, compared with LSSVM and PSO-LSSVM, the model proposed in this paper improves the accuracy of network traffic security prediction, reduces the training time of sample data, and provides strong decision support for network traffic planning and network security management.
AB - In order to improve the accuracy of network traffic prediction and overcome the disadvantages of slow convergence speed and easy to fall into local minimum value in the process of least squares support vector machine (LSSVM) network traffic prediction, a network traffic security prediction model based on LSSVM which simply estimates the width of Gaussian kernel is proposed. The model assigns different Gauss kernel widths for each sampling point according to the local density of the sampling point. The simulation results show that, compared with LSSVM and PSO-LSSVM, the model proposed in this paper improves the accuracy of network traffic security prediction, reduces the training time of sample data, and provides strong decision support for network traffic planning and network security management.
KW - Gauss kernel width
KW - local density of sampling points
KW - LSSVM
KW - network traffic prediction
UR - http://www.scopus.com/inward/record.url?scp=85130692503&partnerID=8YFLogxK
U2 - 10.1504/IJICS.2022.122910
DO - 10.1504/IJICS.2022.122910
M3 - Article
AN - SCOPUS:85130692503
SN - 1744-1765
VL - 18
SP - 1
EP - 11
JO - International Journal of Information and Computer Security
JF - International Journal of Information and Computer Security
IS - 1-2
ER -