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

A construction method and neural network model technology, applied in the construction method of graph neural network, abnormal traffic detection field based on graph neural network, can solve the problem of traffic relationship without much consideration

Pending Publication Date: 2021-02-19
BOYA ZHENGLIAN (BEIJING) TECH CO LTD +2
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Problems solved by technology

However, some existing abnormal traffic models based on machine learning only pay attention to the timing ch

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

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Embodiment Construction

[0036] In order to better understand the present invention, the present invention will be further described below in conjunction with specific embodiments and accompanying drawings.

[0037] The present invention provides a graph neural construction method, including:

[0038] S10: Acquiring features with correlation and timing in the original traffic data;

[0039] S20: converting the features in step S10 into graph structure data;

[0040] S30: Construct a deep graph neural network model.

[0041] The present invention excavates the correlation and timing features in the network traffic in depth, and builds a graph neural network model, on this basis, mines the correlation features between the internal fields of the network traffic message segment, and finally obtains the corresponding data through pre-training. A model with good classification performance for traffic data. For the unknown network traffic to be identified, only a simple session restoration is required, an...

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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...

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Application Information

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IPC IPC(8): H04L29/06G06N3/04G06N3/08
CPCH04L63/1425G06N3/08G06N3/045
Inventor 向鹏李青山孙圣力司华友
Owner BOYA ZHENGLIAN (BEIJING) TECH CO LTD
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