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.  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.
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. 
SSA
At first, Tent mapping with reversal learning, polynomial operator and dynamic mutation strategy are introduced to improve the SSA, which enhances its optimization performance. 
Tent mapping with reversal learning
At first, Tent mapping with reversal learning, polynomial operator and dynamic mutation strategy are introduced to improve the SSA, which enhances its optimization performance. 
polynomial operator
At first, Tent mapping with reversal learning, polynomial operator and dynamic mutation strategy are introduced to improve the SSA, which enhances its optimization performance. 
dynamic mutation strategy
An internet traffic classification method based on echo state network and improved salp swarm algorithm.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.
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