Network traffic classification method and system based on deep learning, and electronic equipment

Abstract

The application relates to a network traffic classification method and system based on deep learning, and electronic equipment. The method comprises the following steps: step a, capturing network traffic sample data; step b, extracting a global feature data set of the network traffic sample data through a deep learning classification algorithm; and step c, constructing a random forest classification model according to the global feature data set, and outputting a network traffic classification result through the random forest classification mode. The random forest classification model is trained by utilizing the extracted global feature, the result shows a stable classification performance, and the ultra-high-dimension traffic data can be processed, and the feature selection is avoided. Compared with the prior art, the high-precision and high-performance of the network traffic classification can be effectively guaranteed; and meanwhile, the classification efficiency can be improved, the training time is shortened, and the computation overhead is reduced.

Description

technical field [0001] The present application belongs to the technical field of network traffic classification, and in particular relates to a network traffic classification method, system and electronic equipment based on deep learning. Background technique [0002] With the rapid development of Internet technology, a large number of new applications continue to appear on the network, and each application carries a variety of services and functions, making the network environment extremely large, complex and changeable. For the normal operation of the network and the real-time allocation of services and resources, it is already an essential part to have an effective method of supervising network activities. Network traffic classification plays an important role in network management, resource allocation, on-demand services, and security systems. For example, for enterprise managers, through fine classification and identification of network traffic, network resources can be...

AI Technical Summary

Problems solved by technology

[0007] To sum up, the existing network traffic classification methods are all based on traditional machine learning technology, and the classification performance is very dependent on the design of traffic characteristics, and how to accurately describe the feature set of traffic characteristics requires a lot of manual design. This is still a difficult point in solving the problem of network traffic classification

At the same time, most of the current network traffic classification methods basically propose various optimization and improvement algorithms for the classification algorithm modules in the training phase, but there is little research and mining on the local features contained in the original data of network traffic. Classification performance is unstable

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