Zhang Meijia, Sun Wenwen, Tian Jie, Zheng Xiyuan, Guan Shaopeng
School of Data Science and Computer Science, Shandong Women's University, Jinan, Shandong, China.
School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China.
PeerJ Comput Sci. 2022 Feb 28;8:e860. doi: 10.7717/peerj-cs.860. eCollection 2022.
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.
互联网流量分类对于网络监控、服务质量和安全至关重要。在本文中，我们提出了一种基于回声状态网络（ESN）的互联网流量分类方法。为了提高识别性能，我们改进了樽海鞘群算法（SSA）以优化ESN。首先，引入带反向学习的帐篷映射、多项式算子和动态变异策略来改进SSA，这提高了其优化性能。然后，利用改进后的SSA来优化ESN的超参数，包括储备池大小、稀疏度、谱半径和输入尺度。最后，采用优化后的ESN对互联网流量进行分类。仿真结果表明，所提出的基于ESN的方法在每类指标和整体准确率方面比其他传统机器学习算法表现要好得多。
