Abstract
Nowadays, the huge traffic generated by a growing number of network applications occupies enormous network bandwidth and increases the burden of network management. The ability to identify and categorize network applications accurately is crucial for learning network traffic conditions, finding people’s online behavior and accelerating the development of the Internet. The prior traffic classification methods often have unstable recognition rate and high computational complexity, which affects the network traffic management and application categories monitoring. Therefore, this paper proposes a method of using the deep learning technology to classify network applications. First, we propose a network application classification model based on Deep Belief Network (DBN). Then we construct a DBN-based model suitable for network applications classification with the Tensorflow framework. Finally, the classification performances of this DBN-based model and the BP-based model are compared on the real data sets. The experimental results show that the applications classification model based on DBN has higher classification accuracy for P2P applications.
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Acknowledgement
This work was supported by a grant from the Key Research and Development Program of Zhejiang (No. 2017C03058), Zhejiang Provincial Key Laboratory of New Network Standards and Technologies (NNST) (No. 2013E10012).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Shao, H., Tang, L., Dong, L., Chen, L., Jiang, X., Wang, W. (2018). A Research of Network Applications Classification Based on Deep Learning. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_2
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DOI: https://doi.org/10.1007/978-3-030-00557-3_2
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