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Real-Time Multi-Application Network Traffic Identification Based on Machine Learning

  • Meihua Qiao
  • Yanqing Ma
  • Yijie Bian
  • Ju Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

In this paper, kinds of network applications are first analyzed, and some simple and effective features from the package headers of network flows are then generated by using the method of time window. What is more, three kinds of machine learning algorithms, which are support vector machine (SVM), back propagation (BP) neural network and BP neural network optimized by particle swarm optimization (PSO), are developed respectively for training and identification of network traffic. The experimental results show that traffic identification based on SVM can not only quickly generate classifier model, but also reach the accuracy of more than 98% under the condition of small sample. Moreover, the method proposed by this paper can measure and identify Internet traffic at any time and meet the needs of identifying real-time multi-application.

Keywords

network traffic identification machine learning SVM BP neural network PSO 

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Meihua Qiao
    • 1
    • 2
  • Yanqing Ma
    • 2
  • Yijie Bian
    • 1
  • Ju Liu
    • 2
  1. 1.Business SchoolHohai UniversityNanjingChina
  2. 2.Suzhou Research InstituteShandong UniversitySuzhouChina

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