Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method and system for recognizing end-to-end flux

A technology of network traffic and P2P traffic, which is applied in the field of communication, can solve the problems of unable to identify P2P traffic, unable to effectively control P2P traffic, and unable to identify

Inactive Publication Date: 2009-08-19
HUAWEI DIGITAL TECH (CHENGDU) CO LTD +1
View PDF0 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The inventor found through research on the prior art that this method can only identify P2P traffic using the Transmission Control Protocol (TCP, Transmission Control Protocol), but cannot identify connections using the User Datagram Protocol (UDP, User Datagram Protocol). Therefore, it is impossible to accurately and comprehensively identify the P2P traffic in the network traffic, and thus cannot effectively control the P2P traffic.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method and system for recognizing end-to-end flux
  • Method and system for recognizing end-to-end flux
  • Method and system for recognizing end-to-end flux

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0028] see figure 1 , the end-to-end traffic identification method provided by Embodiment 1 of the present invention includes the following steps:

[0029] Step 101: Construct a traffic feature vector according to the number of connections and ports, and construct a training sample set according to the feature vector.

[0030] Step 102: Using the training samples in the training sample set to train a support vector machine (SVM) classification model.

[0031] Step 103: Using the SVM classification model to identify P2P traffic from network traffic.

[0032] Through the method provided by the embodiment of the present invention, the traffic feature vector is constructed according to the number of connections and ports, and the constructed feature vector can reflect the P2P traffic characteristics of TCP and UDP, and then use the feature vector to construct a training sample set, and train the support vector machine classification model, and identify network traffic, because t...

Embodiment 2

[0034] see figure 2 , the P2P traffic identification method provided by Embodiment 2 of the present invention includes the following steps:

[0035] Step 201: Construct a feature vector of P2P traffic according to the number of connections and ports.

[0036] By analyzing the difference between P2P traffic and non-P2P traffic, it can be seen that for P2P applications, a certain node is connected to multiple nodes or super nodes in the network, but the number of connections between two nodes is not many. To ensure the load balance of data transmission, the high load between two nodes can be avoided by limiting the number of connections between two nodes, and the communication pressure can be reduced; while for non-P2P applications, the performance between nodes in the network is that a certain node and A small number of nodes in the network are connected, but there are many connections between two nodes.

[0037] Constructing the feature vector of P2P traffic according to th...

Embodiment 3

[0062] In order to establish the SVM classification model, it is necessary to collect a large amount of data on the network as training samples and test samples, but among these numerous samples, the support vectors are effective for establishing the classification model, and other training samples are not helpful to this, so you can Cut the training samples to reduce the size of the training sample set.

[0063] see Figure 4 , the present embodiment provides a clipping processing method, which can be used to clip the normalized training sample set to obtain the clipped training sample set. The clipping processing method includes:

[0064] Step 401: Select the first training sample set from the training sample set for training, and obtain the initial hyperplane of the initial classifier.

[0065] Assume that the training sample set is M, and randomly select a part of samples from M as the first training sample set N. According to the constructed feature vector, SVM training...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The embodiment of the invention discloses an end-to-end flow recognition method and a system thereof, wherein the method comprises the following steps: an eigenvector is constructed according to the linking number and port number and a training sample set is constructed according to the eigenvector; the training sample set is used for training a classification mode of a support vector machine; and the classification mode of the support vector machine is used for recognizing P2P flow from network flow so as to realize accurate and complete recognition of the P2P flow in the network flow and to provide conditions for the further effective control of the P2P flow.

Description

technical field [0001] The invention relates to the technical field of communication, in particular to a peer-to-peer (P2P, Peer to Peer) traffic identification method and system. Background technique [0002] With the continuous expansion of P2P technology applications, especially the growing file sharing business based on P2P systems, the abuse of resources, especially network bandwidth resources, by P2P systems has been highly valued by various network operators and school network managers. Within the education network, Maze is widely used. From the traffic monitoring at the egress of the campus network of Tsinghua University, the traffic of Maze has already surpassed traditional traffic such as the Web, accounting for about 15% of the total traffic. [0003] The continuous increase of P2P services will inevitably lead to huge consumption of bandwidth, which will cause network congestion and reduce network performance. Therefore, identifying P2P traffic and effectively ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): H04L12/56H04L29/06H04L29/08G06K9/62
Inventor 孙知信宫婧严晓倩王飞
Owner HUAWEI DIGITAL TECH (CHENGDU) CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com