Network traffic identification method based on random sampling multi-classifier

Abstract

Based on the network traffic identification method of random sampling multi-classifiers, a network flow data set A with network traffic classification labels and a network flow record set N without network traffic classification labels are given, and network flow records are selected from network flow data set A, Random sampling generates a subset of data B 1 , B 2 ,...,B T , using the decision tree classification algorithm to randomly extract the data subset B 1 , B 2 ,...,B T Carry out learning separately, and record the learning results as T classifiers F 1 , F 2 ,...,F T , randomly extract t classifiers from T classifiers, and use the extracted t classifiers to classify each network flow record in the network flow record set N respectively, and obtain t classification results, and the classification results are counted The classification type with the largest number is used as the classification type of network flow records without network traffic classification labels.

Description

technical field [0001] The technical invention relates to the technical field of network measurement, in particular to a network traffic identification method based on random sampling multi-classifiers. Background technique [0002] The research hotspot of traffic identification technology is the detection technology based on traffic statistical behavior characteristics. The detection technology based on traffic behavior characteristics is a traffic identification technology based on sessions. This type of method does not require any information about the application layer protocol. It is identified by analyzing the statistical measurement of network traffic and using machine learning (Machine Learning) to process traffic. Classification problems are an emerging research direction for this technique. Generally speaking, the research object is a set of sequences with the same five-tuple (source IP, sink IP, source port, sink port, protocol) values, that is, network flow (flo...

AI Technical Summary

Problems solved by technology

Decision trees are used to deal with classification problems. The applicable target variable belongs to categorical variables. It has also been extended to deal with continuous variables, such as the CART model. However, different decision tree algorithms have different requirements and restrictions on data types.

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