Network business flow feature selecting and classifying method based on multi-objective adaptive evolutionary algorithm

Abstract

The invention discloses a network business flow feature selecting and classifying method based on a multi-objective adaptive evolutionary algorithm, comprising the following steps of: firstly sortingthe features by using an information gain ratio and filtering part of irrelevant features to rapidly reduce dimension, then searching a feature space according to the adaptive evolutionary algorithm,using the feature having the top-raking information gain ratio as an initial population, and regarding two target functions of an inconsistent rate and a feature subset dimension as evaluation functions for selecting an optimal feature subset. Adaptive crossover and mutation maintain the population diversity, and the convergence ability of the algorithm is ensured. At the same time, by using a designed three-layer KNN classifier model, the invention classifies six multimedia business flows of online standard-definition live video, web browsing (Baidu), online audio, web browsing (sina), network voice chat and online standard-definition non-live video. The experiment result shows that the invention has higher classification accuracy compared to existing method.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and classification, and in particular relates to a network service flow feature selection and classification method based on a multi-objective adaptive evolution algorithm. Background technique [0002] In recent years, with the rapid development of the Internet, accurate and efficient network traffic classification is an important basis for network management. The diversity of network multimedia service flow types brings great challenges to its classification and identification. Traditional flow classification methods mainly include three types: port-based methods, deep packet inspection methods and methods based on multimedia flow statistics. However, with the emergence of data encryption, new applications and the use of dynamic ports, the first two classification methods will no longer be applicable. Today, most researchers focus on machine learning classification methods includin...

AI Technical Summary

Problems solved by technology

However, when the feature dimension is high, irrelevant and redundant features will increase the time complexity of multi-objective optimization

For evolutionary algorithms, improper selection of population initialization, crossover and mutation probabilities will reduce the final classification accuracy and algorithm convergence ability

And most of the current multi-objective feature selection algorithms have an objective function that is the accuracy of the classifier, so the convergence speed is slow and the running time is long

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