Real-Time Multi-Application Network Traffic Identification Based on Machine Learning
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.
Keywordsnetwork traffic identification machine learning SVM BP neural network PSO
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- 1.Zhao, G., Ji, Z., Xu, C.: Survey of Techniques for Internet Traffic Identification. Journal of Chinese Computer Systems 31(8), 1514–1520 (2010) (in Chinese)Google Scholar
- 2.Schulzrinne, H., Casner, S., Frederick, R., et al.: RTP: A Transport Protocol for Real-Time Applications. RFC 1889, IETF (1996)Google Scholar
- 4.Sen, S., Spatscheck, O., Wang, D.: Accurate, scalable in network identification of P2P traffic using application signatures. In: Proc. of 13th International Conference on World Wide Web (WWW), New York, NY, USA (May 2004)Google Scholar
- 7.Karagiannis, T., Papagiannaki, K., Faloutsos, M.: BLINC: multilevel traffic classification in the dark. In: ACM SIGCOMM, Philadelphia, PA (2005)Google Scholar
- 9.Moore, A.W., Zuev, D.: Discriminators for use in Flow-based classification. Technical Report IRC-TR-04-028, Intel Research, Cambridge (2004)Google Scholar
- 10.Ma, Y.: Methods and Implementations of Network Traffic Identification Based on Machine Learning. Master thesis, Shandong University (2014) (in Chinese)Google Scholar
- 11.Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machine An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Publishing House of Electronics Industry, Beijing (2004) (Chinese Version, Translated by G. Li, M. Wang, H. Ceng)Google Scholar
- 13.Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
- 14.Zhang, G., Li, Y.M.: Cooperative particle swarm optimizer with elimination mechanism for global optimization of multimodal problems. In: Proceedingds of IEEE Congress on Evolutionary Computation (CEC), Beijinag, China, pp. 210–217 (2014)Google Scholar
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