Traffic identification and traffic analysis based on support vector machine

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.