Network traffic classification method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a network traffic classification method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a target classification model trained based on a loss function; classifying traffic data to be classified based on the target classification model; wherein the loss function is determined based on a weight parameter of atraining sample in the network traffic data. According to the method of the invention, the loss function is determined through the training sample weight parameters based on the classification model,and the to-be-classified flow data is classified through the target classification model trained by the loss function, so that the problems of sample imbalance and positive and negative sample proportion imbalance during model training are solved, the classification performance of a subclass data set is ensured, and accuracy of classification results is improved.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a network traffic classification method, device, electronic equipment and storage medium. Background technique [0002] Among the many Internet traffic, the distribution of different types of traffic is not uniform. Regardless of whether it is encrypted, service type, or application type, each category has a different proportion. For example, in malicious traffic Among the identified applications, some malicious traffic belongs to small categories. However, in the relevant research on network traffic classification based on deep learning, the basic assumption is that the training samples conform to the uniform distribution. If the model is trained in the state of unbalanced data categories, the classification results of the model will usually make the classification results of the model more conducive to guaranteeing a large number of data. The large-sample classificati...

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Problems solved by technology

[0005] Embodiments of the present invention provide a network traffic classification method, device, electronic equipment, and storage medium, which are used to solve the defects of poor classification performance and inaccurate classification results of small and medium-sized data sets in the prior art, and solve the problem of positive and negative data during model training. The problem of unbalanced sample ratio ensures the classification performance of small class data sets and provides the accuracy of classification results

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