Abstract
How to accurately and efficiently complete the classification of Network data stream is an important research topic and a huge challenge in the field of Internet data analysis. Traditional port-based and DPI-based classification methods have obvious disadvantages in the increase category of P2P services and the problem of poor encryption resistance, leading to a sharp drop in classification coverage. Based on the original DPI classification, this paper proposes a method of network data stream classification using the combination of DPI and machine learning. This method uses DPI to detect network data streams of known features and uses machine learning methods to analyze unknown features and encrypted network data streams. Experiments show that this method can effectively improve the accuracy of network data stream classification.
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Acknowledgements
This work was funded by the National Natural Science Foundation of China (61772282, 61772454, 61373134, 61402234). It was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0901) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET). We declare that we do not have any conflicts of interest to this work.
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Yin, C., Wang, H., Wang, J. (2019). Network Data Stream Classification by Deep Packet Inspection and Machine Learning. In: Park, J., Loia, V., Choo, KK., Yi, G. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2018 2018. Lecture Notes in Electrical Engineering, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-13-1328-8_31
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DOI: https://doi.org/10.1007/978-981-13-1328-8_31
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