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Parallel network flow classification method

A network traffic and classification method technology, applied in the field of parallel network traffic classification, can solve the problem that computing node resources cannot efficiently solve large-scale data processing, etc.

Active Publication Date: 2015-06-10
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

[0006] What the present invention aims to solve is that the current single computing node resources cannot efficiently solve the problem of large-scale data processing and provide a parallel network traffic classification method

Method used

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Embodiment Construction

[0061] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0062] A parallel network traffic classification method such as figure 1 As shown, it is completed through two Map-Reduce processes: the first Map-Reduce process performs feature selection on network traffic data, eliminating irrelevant and redundant features; the second Map-Reduce process uses selective integration The learning algorithm classifies the network traffic to obtain a classification result.

[0063] Such as figure 2 As shown, the process of network traffic feature selection based on the MapReduce parallel framework first pre-selects feature vector subsets with strong discriminative ability and high correlation through the fisher score and the normalized value of class label mutual information, and then through feature mutual information The normalized value removes redundant features in the selected subset of feature vectors.

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Abstract

The invention discloses a parallel network flow classification method. Based on a MapReduce parallel frame provided by a Hadoop cluster platform, a data set is firstly pre-processed, and high-dimensional network flow data dimension reduction is performed by means of a feature selection approach to remove uncorrelated and redundant characteristics; multiple base classifiers are trained through selective ensemble learning, the high-accuracy and large-difference base classifier ensembles are selected; finally a final classifying result is obtained in a majority voting mode. By means of the parallel network flow classification method, the mass data dimension reduction and classification problems can be effectively solved, and data processing efficiency is improved to the great extent.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a parallel network flow classification method. Background technique [0002] With the rapid development of high-speed networks, new types of network services are constantly emerging, and the network scale is continuously expanding due to its openness and sharing. Different application traffic presents different characteristics. The field of classification poses enormous challenges. Network traffic classification is an important basis for understanding, managing and optimizing various network resources. It classifies the bidirectional TCP flow or UDP flow generated by the Internet based on the TCP / IP protocol according to the type of network application (such as WWW, FTP, MAIL, P2P, etc.) Classification. [0003] Network traffic feature selection is a key step in traffic classification. In the case of loss of less information, irrelevant or redundant features...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/26G06F17/30
Inventor 王勇龙也陶晓玲何倩韦毅曾小宝
Owner GUILIN UNIV OF ELECTRONIC TECH
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