Feature database generation method and device as well as corresponding flow sorting method and device

Abstract

The invention discloses a feature database generation method. The method comprises the following steps: extracting target flow corresponding to a target application; initially classifying the target flow under a preset dimension; extracting features from the classified target flow, and matching the extracted features with a saved history feature database in order to perform function prejudgment onthe target flow; generating an analysis tree specific to the target flow by taking each pre-judgment result as a branch; and generating a new feature database according to the analysis tree. The invention also provides a feature database generating device as well as a flow sorting method and device applying the feature database. Through adoption of the feature database generation method and device as well as the flow sorting method and device, the flow matching accuracy and efficiency can be optimized according to feature matching during flow sorting of the feature database.

Description

technical field [0001] The present invention relates to the technical field of network optimization and traffic identification, in particular to a method and device for generating a feature database, and a method and device for sorting traffic. Background technique [0002] With the great popularization of the Internet in recent years and the diversification of the application market, the personalized needs of users have also increased. This requires the network optimization platform to have the ability to operate in a refined manner, and to manipulate, adjust, and optimize network resources in a smaller granularity. At present, a large number of services of different natures are encapsulated on the standard IPV4 protocol, and the preliminary traffic identification only relying on the third and fourth layer protocols is far from meeting the demand. Deep Packet Inspection (DPI) equipment is used to further identify Features over four floors. The common access mode of DPI equ...

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Problems solved by technology

[0003] However, this method has the following disadvantages: In the above identification process, for the DPI device operating on the network optimization platform, it is the passive receiver of traffic, that is, real-time analysis of traffic when it arrives, and the system's hardware performance and internal Logical efficiency puts high demands on

Moreover, the future trend of network traffic will inevitably develop in the direction of greater bandwidth demand. In the case of heavy traffic, feature matching will consume high system performance and may affect the quality of online services.

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