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Self-adaptive semi-supervised network traffic classification method, system and equipment

An adaptive network and traffic classification technology, applied in the field of self-adaptive semi-supervised network traffic classification, can solve problems such as the inability to realize system parameter adaptation and the inability to automatically determine the best parameters, so as to improve the classification accuracy and ensure reliability. and accuracy, high purity effect

Active Publication Date: 2018-03-27
BEIJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is: the existing optimal parameters cannot be automatically determined in the training stage, manual parameter adjustment is required, and the parameter self-adaptation of the system cannot be realized.

Method used

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  • Self-adaptive semi-supervised network traffic classification method, system and equipment
  • Self-adaptive semi-supervised network traffic classification method, system and equipment
  • Self-adaptive semi-supervised network traffic classification method, system and equipment

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Embodiment 8

[0082] Such as image 3 As shown, the present invention also provides an adaptive semi-supervised network traffic classification system, which includes:

[0083] Acquisition module, vector set processing module, clustering module, classification module, output module;

[0084] The obtaining module is used to obtain marked and unmarked network flows, extract a preset fixed amount of flow characteristics in each network flow, and obtain network flow feature vectors;

[0085] The vector set processing module is used to calculate the centroid of the network flow feature vector set in each type according to the marked network flow feature vector, and obtain the vector set M;

[0086] The clustering module is used to use the vector set M as the initial center point of k-means clustering, and perform adaptive semi-supervised k- means clustering, and output k-means clustering;

[0087] The classification module is configured to map the obtained network flows in each type of cluster...

Embodiment 10

[0094] Such as Figure 4 As shown, the embodiment of the present invention also provides a computer device, which includes: a processor, a memory, and a computer program stored on the memory and operable on the processor, wherein the processing When the program is executed by the computer, the steps of the method described in any one of the foregoing embodiments 1 to 7 are implemented.

[0095] It should be noted that, in this embodiment 10, the computer equipment of the present invention is used to obtain marked and unmarked network flows, extract a preset fixed amount of flow features in each network flow, and obtain network flow feature vectors, According to the marked network flow feature vector, calculate the center point of each type of network flow that has been marked, and use the center point as the initial clustering center M of the k-means algorithm, for the mixed marked types and Semi-supervised k-means clustering is performed on the unlabeled network flow point s...

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Abstract

The invention relates a self-adaptive semi-supervised network traffic classification method, system and equipment. The method comprises the following steps: acquiring a network stream, extracting thestream feature with the preset fixed quantity in each network stream to obtain a network stream feature vector; computing the centroid of the network stream feature vector in each type according to the marked network stream feature vector, thereby obtaining a vector set M; performing self-adaptive semi-supervised k-means clustering by taking the vector set M as an initial center point; mapping theobtained network stream in each type of cluster to the belonged traffic type according to the maximum posterior probability; taking the traffic cluster of the known type as the training data to trainan online traffic classifier. The invention further relates to a system, the system comprises an acquisition module, a vector set processing module, a clustering module, a classification module, andan output module. The invention relates to the equipment. The equipment comprises a processor, a memorizer, and a computer program stored on the memorizer and capable of being run on the memorizer.

Description

technical field [0001] The invention belongs to the field of network flow management, in particular to an adaptive semi-supervised network flow classification method, system and equipment. Background technique [0002] Most traditional network flow-based methods use supervised or unsupervised machine learning algorithms to implement network traffic classification. In supervised traffic classification, a learning engine takes a set of labeled flow samples, trains against predefined protocol categories, and returns a trained classification model that can predict the protocol type of future network flows. However, with the rapid expansion of the network, many new applications are deployed on the Internet, and the unknown flows corresponding to these applications cannot be handled by supervised learning-based classification methods. In this case, the unknown traffic will be wrongly classified into some predefined traffic class and affect the overall accuracy of the classifier. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/26H04L12/851G06K9/62
CPCH04L43/026H04L43/062H04L47/2441H04L47/2483G06F18/23213
Inventor 冉静孔晓晨刘元安胡鹤飞袁东明
Owner BEIJING UNIV OF POSTS & TELECOMM
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