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A deep learning-based sdn flow table conflict detection device and method

A deep learning and deep detection technology, applied in the field of network communication, can solve the problem that deep learning is not widely used

Active Publication Date: 2019-08-27
ZHEJIANG GONGSHANG UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, at present, deep learning has not been widely used in the network environment under the SDN architecture, especially the application of deep learning to the security field of SDN

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  • A deep learning-based sdn flow table conflict detection device and method
  • A deep learning-based sdn flow table conflict detection device and method

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Embodiment Construction

[0028] In a large SDN network, a large number of complex policies need to be deployed. If these policies are deployed by the network administrator, it will be time-consuming and laborious. The automatic deployment of policies in SDN networks is a future trend, and the automatic deployment of SDN policies will greatly save the time and cost of network administrators. The configuration problem in the SDN network is a kind of problem in the SDN security threat, and the policy conflict is one aspect of this kind of problem. Combining the above two situations, a method that can quickly detect conflicts in SDN policy deployment is needed to solve some security problems in SDN networks. The present invention provides an SDN flow table conflict detection device and method based on deep learning, which is mainly used to solve the problem of conflict detection when automatically deploying policies in a large-scale SDN network. The present invention will be further described below in co...

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Abstract

The invention discloses an SDN flow table conflict detection device and method based on deep learning. The device includes the following modules: a window division module, a depth detection module, a result analysis module, a monitoring module and a data storage module. The method includes: the specific SDN application is mapped to a specific SDN flow entry {P} through the policy mapping module; the window segmentation module extracts the flow entry {W} with a window size suitable for processing by the depth detection module from the database storage module; the {P } and {W} are input to the depth detection module for flow table conflict detection; the flow table after the conflict detection module is input to the result analysis module for analysis and judgment. The present invention utilizes the characteristics of deep learning to abstract high-level data and automatic learning, and compared with traditional conflict search algorithms, it can more quickly detect whether super-large-scale flow entries are conflicted during large-scale application deployment.

Description

technical field [0001] The present invention relates to the technical field of network communication, in particular to a deep learning-based SDN flow table conflict detection device and method. Background technique [0002] As people's demand for network applications increases and the scale of data centers continues to expand, network operators and service providers are facing huge challenges in terms of cost, management, and complexity of traditional network infrastructure. The high cost of traditional network architecture deployment restricts the construction of data centers, and the complexity delays the time to market for new services and applications. The difficulty in managing network equipment in the traditional network architecture further increases the operating costs of enterprises and reduces the response speed of updating the network structure. Faced with the problems of high cost, high complexity, and low flexibility exposed by traditional network architectures...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/24H04L12/741H04L45/74
CPCH04L41/0873H04L45/745
Inventor 李传煌程成金蓉王伟明岑利杰
Owner ZHEJIANG GONGSHANG UNIVERSITY