Abstract
Billions of users access the Internet through their mobile devices to get services. Mobile traffic classification has become a hot topic in recent years due to its large volume of traffic data. Many of the studies that have been done show that the key point of mobile traffic identification is to extract signatures. However, the process of signature extraction is usually too complex to perform. In this paper, we propose a novel method RFGRU which is based on the Random Forest and gated recurrent unit, to address the mobile traffic classification problem. Several experiments are performed to verify the effectiveness of RFGRU. The results show that RFGRU delivers a good recognition rate and can accurately identify the traffic of the mobile applications.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (No. 61702288), the Natural Science Foundation of Tianjin in China (No. 16JCQNJC00700), the National Information Security Research Plan of China, and the Fundamental Research Funds for the Central Universities.
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Zhang, Y., Jin, Y., Zhang, J., Wu, H., Zou, X. (2018). RFGRU: A Novel Approach for Mobile Application Traffic Identification. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11335. Springer, Cham. https://doi.org/10.1007/978-3-030-05054-2_38
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