SDN (Software Defined Networking) controller for flow classification based on DPI (Deep Packet Inspection) and machine learning algorithm

A technology of traffic classification and machine learning, applied in the direction of instruments, calculations, computer components, etc., to achieve the effect of ensuring richness, accuracy and classification accuracy

Inactive Publication Date: 2019-03-08
QINGDAO TECHNOLOGICAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] In order to solve the shortcomings of the existing traffic classification methods, the present invention designs a SDN controller based on DPI and machine learning algorithms for traffic classification

Method used

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  • SDN (Software Defined Networking) controller for flow classification based on DPI (Deep Packet Inspection) and machine learning algorithm
  • SDN (Software Defined Networking) controller for flow classification based on DPI (Deep Packet Inspection) and machine learning algorithm
  • SDN (Software Defined Networking) controller for flow classification based on DPI (Deep Packet Inspection) and machine learning algorithm

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Embodiment 1

[0040] An embodiment of the present invention is provided below, explaining how the controller in the present invention completes the classification process of a data stream (such as image 3 , Figure 4 , Figure 5 shown), the specific packet scheduling steps are as follows:

[0041] Step SA01: After the controller obtains data packets from the switch through the Packet_in operation, the existing functional modules of the controller can calculate the size of each data packet x i

[0042] Step SA02: Taking the difference in the size of the data packet as the main analysis element in the clustering, firstly select the initial value of 64, 128, 256, 512, 1024, 2000 as the initial mean value vector μ before the algorithm starts.

[0043] Step SA03: Calculate the Euclidean distance d between the size of the obtained data packet and each mean value vector i =||x i -μ|| 2 , select the mean vector with the closest distance to determine x i cluster label

[0044] Step SA04: Ca...

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Abstract

The invention discloses an SDN (Software Defined Networking) controller for flow classification based on DPI (Deep Packet Inspection) and a machine learning algorithm. In an existing controller module, a DPI mechanism and a machine learning algorithm are combined by adding a self-designed module to perform an application request classification function of a flow data packet. The self-designed module mainly consists of three parts: a data flow feature building module, a feature extraction and classifier training module, and a classifier-based flow classification module. Firstly, a K-means clustering algorithm is used to construct an information feature base; secondly, a DPI technique is used to extract features to form the training module; and finally, the Naive Bayes algorithm is used to perform detailed flow classification on data packets. The SDN controller for flow classification based on the DPI and the machine learning algorithm, which is disclosed by the invention, is relativelyhigh in classification precision and relatively good in flexibility.

Description

technical field [0001] The invention relates to an SDN controller, in particular to an SDN controller which combines deep packet detection technology and machine learning algorithm to classify network traffic. Background technique [0002] SDN (Software Defined Networking) is a new computer network model. Compared with the traditional network architecture system, SDN decouples the network control layer from the data layer, breaking the vertical framework of the OSI model. figure 1 The architecture diagram of the SDN network is shown. The OpenFlow (OF) protocol is an important communication protocol through which controllers and network switches interact. OpenFlow is also the most commonly used southbound interface protocol in SDN. OF switches contain single or multi-level flow tables. A flow table is composed of flow information. Each flow table contains some rules and actions to be executed. [0003] SDN is considered as a future network paradigm, which can significantl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/26H04L12/851G06K9/62
CPCH04L43/026H04L43/028H04L47/2441G06F18/23213G06F18/2155G06F18/24155
Inventor 李道全王雪于波黄泰铭
Owner QINGDAO TECHNOLOGICAL UNIVERSITY
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