Establishment method of dark web traffic recognition model based on SVM machine learning

Abstract

The invention discloses an establishment method of a dark web traffic recognition model based on SVM machine learning. The method comprises the following steps: constructing a traffic detection model based on the SVM machine learning; performing machine learning on a parameter in the traffic detection model to obtain four feature values of pure anonymous traffic and pure non-anonymous traffic; substituting four feature values of the pure anonymous traffic and the pure non-anonymous traffic into a traffic detection model to perform the operation so as to obtain a parameter of the traffic detection model. Compared with the prior art, the establishment method disclosed by the invention has the advantages as follows: a mathematic model for anonymous data traffic recognition can be accurately depicted by use of the method disclosed by the invention, the method can be applied to the anonymous network data traffic detection, the detection accuracy rate is high, the operation is simple and efficient; after the anonymous network is upgraded, the new anonymous network data traffic can be detected only needing to relearning the upgraded anonymous network by use of the algorithm based on the machine learning.

Description

technical field [0001] The invention relates to a method for establishing a darknet traffic identification model based on SVM machine learning. Background technique [0002] The analysis and control of anonymous network (darknet) traffic, especially the traffic detection, is currently in the exploratory research stage. At present, there is no method that can effectively detect all anonymous network traffic, and some methods may only be effective for a certain anonymous network. It is only valid for a certain version, so the detection of anonymous network traffic is an eternal research topic, and continuous follow-up research is required to cope with the continuous upgrading and changes of the anonymous network, and to improve the accuracy of anonymous network traffic detection, the key lies in the traffic The accuracy of the recognition model is established. This method adopts the method of machine learning, establishes a mathematical model of anonymous network traffic iden...

AI Technical Summary

Problems solved by technology

[0002] The analysis and control of anonymous network (dark network) traffic, especially traffic detection is currently in the exploratory research stage. At present, there is no method that can effectively detect all anonymous network traffic. Some methods may only be effective for certain anonymous networks. It is even only valid for a certain version, so the detection of anonymous network traffic is an eternal research topic, which requires continuous follow-up research to cope with the continuous upgrading and changes of anonymous networks. The key to improving the accuracy of anonymous network traffic detection lies in the traffic The accuracy of the recognition model

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