Traffic identification and feature extraction method based on deep learning

Abstract

The invention discloses a traffic identification and feature extraction method based on deep learning. The method comprises the steps of data packet capture, data set establishment, convolutional neural network establishment, model training, model self-study and optimization, and network data packet feature extraction. According to the method, the good performance of the convolutional neural network in data processing application is fully utilized, and the convolutional neural network which is rapid and accurate and is suitable for network message processing is designed; and flow classification prediction is carried out by utilizing the trained model, data packets with insufficient probabilities of prediction errors and classification under a correct type in a result are selected out and re-fused into a training set training model, thereby realizing autonomous optimization of the model. According to the method, a class activation mapping method is utilized to carry out feature extraction on the traffic, extracted feature fields can enable people to know the features of data packets of specific types, and the feature fields not only can be used for a traditional DPI technology, butalso are suitable for application scenarios where DPI traffic classification has been deployed.

Description

technical field [0001] The present invention relates to the technical field of data deep learning algorithms, in particular to a traffic identification and feature extraction method based on deep learning. Background technique [0002] Network traffic classification is an important task in modern communication networks. It provides judgment basis and underlying technical support for application fields such as network resource allocation, network intrusion detection, malware detection, operator regulation and pricing. At the same time, with the development of technologies such as SD-WAN and SRv6, the provision of personalized network services and traffic engineering have put forward higher requirements for traffic classification technology. The vigorous development of today's mobile Internet and the emergence of a large number of new network applications have resulted in today's network traffic showing the characteristics of large-scale network traffic data, various types of ...

AI Technical Summary

Problems solved by technology

However, there are still some problems that have not been resolved: 1. The classification accuracy on data sets with a large number of classifications needs to be improved; 2. The model is trained with pre-prepared data sets. Data packet optimization model; 3. The interpretability of deep learning application on traffic classification is relatively poor. The trained neural network model can only perform traffic classification, wasting the rich information inside the model

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