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.
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Qiao, M., Ma, Y., Bian, Y., Liu, J. (2015). Real-Time Multi-Application Network Traffic Identification Based on Machine Learning. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_52
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DOI: https://doi.org/10.1007/978-3-319-25393-0_52
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