SVM network business classification method

Abstract

The invention provides an SVM network business classification method. The method includes 1, utilizing a serial segmentation feedback method to train an initial SVM classifier of network business, and acquiring a classification hyperplane; 2, by means of the effective boundary elimination method, eliminating sample points useless for classification hyperplane construction, and acquiring the optimal classification hyperplane. The efficient method is adopted to optimize the machine learning algorithm, on the premise of guaranteeing the constant classification accuracy, the training time of the machine learning classifier is shortened effectively, and the classification cost of network traffic data is reduced.

Description

technical field [0001] The invention relates to a method for classifying network services, in particular to a method for classifying SVM network services. Background technique [0002] The web2.0 era has brought a brand-new rapid development to the Internet. The types and quantities of network applications are extremely rich, the number of netizens has increased sharply, and the network traffic has doubled. However, how to provide users with a better user experience under the premise of limited network resources has become a new research hotspot. Many network analysis software such as sniffer and Wireshark have come out one after another, highlighting the increasingly important classification of network services. [0003] The traditional network classification method is already stretched in the face of today's complex and changeable network environment. The accuracy of the traditional port analysis method is greatly reduced due to the widespread use of dynamic port technolo...

AI Technical Summary

Problems solved by technology

[0003] The traditional network classification method is already stretched in the face of today's complex and changeable network environment. The accuracy of the traditional port analysis method is greatly reduced due to the widespread use of dynamic port technology. The deep packet inspection DPI method is due to the current encryption algorithm and P2P business. The use of a large number of proprietary protocols has become difficult

The machine learning methods that have emerged in recent years have not been able to come up with good solutions. There are various problems in the training of classifiers and the fitting of classifiers.

The SVM algorithm based on the VC dimension theory of machine learning and the principle of structural risk minimization reflects the original intention of pursuing learning ability and model complexity, but because its classifier training time complexity is high and it is designed for two-dimensional classification , its application in network classification has always been weak

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