Graph neural network construction method and abnormal flow detection method based on graph neural network

Abstract

The invention provides a graph neural network construction method and an abnormal flow detection method based on a graph neural network. The graph neural network construction method for abnormal flowdetection comprises the steps of S10, obtaining features with correlation and timing in original flow data; S20, converting the features in the step S10 into graph structure data; and S30, constructing a depth map neural network model. The features with correlation and time sequence in the network flow are deeply mined, the graph neural network model is constructed, the related features between the internal fields of the network flow message segment are mined on the basis, and the model with a good classification effect on the flow data is finally obtained through pre-training. For unknown network traffic to be identified, the unknown network traffic to be identified can be quickly analyzed and judged through the trained model only by performing simple session restoration, and possible anomalies in traffic data can be quickly found.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a graph neural network construction method and a graph neural network-based abnormal traffic detection method. Background technique [0002] With the rapid development of machine learning technology and the rapid rise of the artificial intelligence industry, traffic anomaly detection using machine learning and deep learning methods has become the focus of attention in the industry and academia. In the field of malicious traffic analysis, based on machine learning methods, construct abnormal traffic Traffic classification and detection using the model has gradually become the mainstream of research in the industry in recent years. The traffic classification and detection model based on the deep learning algorithm also has a good effect in traffic identification. The deep learning method learns high-level semantic features from the original traffic data through the deep n...

AI Technical Summary

Problems solved by technology

However, some existing abnormal traffic models based on machine learning only pay attention to the timing characteristics of the traffic, but do not consider the relationship between the fields of the traffic itself.

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