Chaotic prediction of network traffic based on support vector machine optimized by improved beetle swarm optimization algorithm

Haiyan Hu; Qiaoyan Kang; Shuo Zhao; Jianfeng Wang; Youbin Fu

doi:10.1117/12.2657090

28 November 2022 Chaotic prediction of network traffic based on support vector machine optimized by improved beetle swarm optimization algorithm

Haiyan Hu, Qiaoyan Kang, Shuo Zhao, Jianfeng Wang, Youbin Fu

Author Affiliations +

Proceedings Volume 12503, International Conference on Network Communication and Information Security (ICNCIS 2022); 125030R (2022) https://doi.org/10.1117/12.2657090
Event: International Conference on Network Communication and Information Security (ICNCIS 2022), 2022, Qingdao, China

Abstract

Network traffic prediction is the key to network security management and improving network operation speed. This paper proposes a network traffic chaotic prediction method based on the improved beetle swarm algorithm and optimized support vector machine. Firstly, the new network traffic time series is obtained by the phase space reconstruction method. Then the SVM is optimized by the improved beetle swarm algorithm, and the optimized SVM is used to predict the chaos of the network traffic. Finally, the improved method is compared with the experimental results of network traffic chaos prediction based on particle swarm optimization support vector machine. The results show that the algorithm proposed in this paper has better results in terms of convergence effect and prediction accuracy.

Citation Download Citation

Haiyan Hu, Qiaoyan Kang, Shuo Zhao, Jianfeng Wang, and Youbin Fu "Chaotic prediction of network traffic based on support vector machine optimized by improved beetle swarm optimization algorithm", Proc. SPIE 12503, International Conference on Network Communication and Information Security (ICNCIS 2022), 125030R (28 November 2022); https://doi.org/10.1117/12.2657090

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