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

Abstract

As the number of applications based on the Internet is increasing, the traffic becomes more and more complex. So how to improve the service quality and security of the network is becoming more and more important. This paper studies the application of support vector machine in traffic identification to classify the network traffic. Through data collection and feature generation methods and network traffic feature screening methods, support vector machine is used as a classifier by using the generalization capability of support vector machine, and the parameters and kernel functions of the support vector machine 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 support vector machine 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.