NETWORK TRAFFIC CLASSIFICATION METHOD AND SYSTEM BASED ON DEEP LEARNING, AND ELECTRONIC DEVICE

摘要

The present application relates to a network traffic classification method and system based on deep learning, and an electronic device. The method comprises: step a: capturing network traffic sample data; step b: extracting a global feature data set of the network traffic sample data by means of 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 by means of the random forest classification model. In the present application, the random forest classification model is trained by utilizing extracted global features, the result shows stable classification performance, ultra-high-dimension traffic data can be processed, and feature selection is not necessary. Compared with the prior art, the present application can effectively guarantee high precision and high performance of network traffic classification; in addition, the classification efficiency can be improved, the training time can be shortened, and the computation overhead can be reduced.