Retracted: Traffic identification and traffic analysis based on support vector machine

Abstract

Summary The number of applications based on the Internet is increasing, which results the traffic becoming more and more complex. Therefore, how to improve the service quality and security of the network is becoming more and more important. This paper studies the application of SVM in traffic identification to classify network traffic. Through data collection and feature generation methods and network traffic feature screening methods, SVM is used as a classifier by using the generalization capability of SVM, and the parameters and kernel functions of the SVM are adjusted and selected based on cross comparison ideas and methods. Using the cross‐validation method to make the most reasonable statistics for the classification and recognition accuracy of the adjusted support vector machine avoids the situation that the classification accuracy of the support vector machine is unstable or the statistics are inaccurate. Finally, a traffic classification and identification system based on SVM is realized. The final recognition rate of encrypted traffic is up to 99.31%, which overcomes the disadvantages of traditional traffic identification and achieves a fairly reliable accuracy.

A Comparative Study of Traffic Classification Techniques for Smart City Networks

“…Zhongsheng et al [ 26 ] proposed an SVM to classify network traffic in campus backbone networks. They applied the SVM to traffic classification through data collection and feature generation.…”

Explainable Internet Traffic Classification

“…One of the first significant work on the application of SVM to traffic classification is [19], where the authors apply one of the approaches to solving multi-class problems with SVMs and describe a simple optimization algorithm that allows the classifier to perform correctly with as little training as a few hundred samples. Since then, many other works have proposed SVM-based methods [4,[20][21][22][23] and, as a result, SVM is nowadays considered as a de facto standard in the field. Nonetheless, as already discussed, all of these works propose methods based on black-box models that do not provide any information about the classification criteria.…”

Retraction Note to: Traffic identification and traffic analysis based on support vector machine

“…The Editor-in-Chief has retracted this article because it shows significant overlap with a previously published article by Zhongshen et al [1].…”