Method and device for classifying network traffic on basis of grey wolf algorithms

Abstract

An embodiment of the invention discloses a method and a device for classifying network traffic on the basis of grey wolf algorithms. The method for classifying the network traffic on the basis of the grey wolf algorithms in the embodiment of the invention includes S1, carrying out multilevel and multi-scale preprocessing decomposition on network traffic data by means of wavelet packet transformation; S2, optimizing FCM (fuzzy C-means) clustering algorithm models by the aid of novel swarm intelligence algorithms-grey wolf horizontal-vertical multi-dimensional chaos optimization algorithms and classifying decomposed network traffic data by the aid of the FCM clustering algorithm models. The method and the device have the advantage that the technical problems of low decomposition accuracy and influence on network traffic clustering and classification in late periods due to the fact that existing network traffic is only decomposed on low-frequency portions by means of wavelet transformation when multi-scale decomposition is carried out on the existing network traffic by the aid of existing methods for classifying the existing network traffic can be solved by the aid of the method and the device, and the technical problems of low network traffic classification and identification accuracy and efficiency of models due to the fact that the clustering optimization models on the basis of swarm intelligence algorithms are easy to fall into local optimal solution and are low in convergence speed also can be solved by the aid of the method and the device.

Description

technical field [0001] The invention relates to the field of network traffic classification, in particular to a gray wolf algorithm-based network traffic classification method and device. Background technique [0002] Accurate network traffic classification and identification is not only the basis for traffic engineering implementation, application service differentiation, and user behavior monitoring, but also an important guarantee for business structure optimization, QoS guarantee, and network security management. The traditional traffic identification method is realized based on the fixed service port designated by the Internet Assigned Numbers Bureau, and the application protocol of traffic is identified through different service ports. However, with the widespread application of P2P technology and private protocols, a large number of application protocols use dynamic ports to realize data transmission, which makes the traditional identification method based on service ...

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present invention provides a network traffic classification method and device based on the gray wolf algorithm, which solves the problem of multi-scale decomposition of network traffic in the existing network traffic classification method. Decomposition, the accuracy of the decomposition is not high enough, which affects the clustering and classification of the network traffic in the later stage. The clustering optimization model based on the swarm intelligence algorithm is easy to fall into the local optimal solution, and the convergence speed is slow. technical problems of accuracy and inefficiency

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