An internet traffic classification method based on echo state network and improved salp swarm algorithm

Meijia Zhang; Wenwen Sun; Jie Tian; Xiyuan Zheng; Shaopeng Guan

doi:10.7717/peerj-cs.860

An internet traffic classification method based on echo state network and improved salp swarm algorithm

PeerJ Comput Sci. 2022 Feb 28:8:e860. doi: 10.7717/peerj-cs.860. eCollection 2022.

Authors

Meijia Zhang¹, Wenwen Sun², Jie Tian¹, Xiyuan Zheng¹, Shaopeng Guan²

Affiliations

¹ School of Data Science and Computer Science, Shandong Women's University, Jinan, Shandong, China.
² School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China.

Abstract

Internet traffic classification is fundamental to network monitoring, service quality and security. In this paper, we propose an internet traffic classification method based on the Echo State Network (ESN). To enhance the identification performance, we improve the Salp Swarm Algorithm (SSA) to optimize the ESN. At first, Tent mapping with reversal learning, polynomial operator and dynamic mutation strategy are introduced to improve the SSA, which enhances its optimization performance. Then, the advanced SSA are utilized to optimize the hyperparameters of the ESN, including the size of the reservoir, sparse degree, spectral radius and input scale. Finally, the optimized ESN is adopted to classify Internet traffic. The simulation results show that the proposed ESN-based method performs much better than other traditional machine learning algorithms in terms of per-class metrics and overall accuracy.

Keywords: Classification; Echo state network; Hyperparameter optimization; Internet traffic; Salp swarm algorithm.

Grants and funding

This work was supported by the Talent Project of Shandong Women’s University under Grant 2020RCYJ21, 2018GSPGJ08, 2018RC34061, the National Science Foundation of China under Grant 62006143, and the National Science Foundation of Shandong Province (ZR2020MF152). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.