Abstract
Recently, with the progress of research on accurate traffic classification (TC), the major obstacle to achieving accurate TC is the lack of an efficient ground truth (GT) generation method. A firm GT is important for exploring the underlying characteristics of network traffic, building the traffic model, and verifying the classification result, etc. However, current existing GT generation methods can only be made manually or with additional high-cost DPI (deep packet inspection) devices. They are neither too complicated nor too expensive for research community. In response to this problem, we present LCGT, a low-cost continuous GT generation method for TC. Based on LCGT, we propose a novel updateable TC system, which can always reflect the features of up-to-date traffic. While we have found LCGT to be very useful in our own research, we seek to initiate a broader discussion to guide the refinement of the tools. LCGT is located on: http://code.google.com/p/traclassy
China 973 Programme (No. 2009CB320505), Project 60811140347 supported by NSFC-KOSEF, Specialized Research Fund for the Doctoral Program of Higher Education(200800130014), Project 60772111 supported by National Natural Science Foundation of China.
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Tian, X., Huang, X., Sun, Q. (2009). LCGT: A Low-Cost Continuous Ground Truth Generation Method for Traffic Classification. In: Hong, C.S., Tonouchi, T., Ma, Y., Chao, CS. (eds) Management Enabling the Future Internet for Changing Business and New Computing Services. APNOMS 2009. Lecture Notes in Computer Science, vol 5787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04492-2_22
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DOI: https://doi.org/10.1007/978-3-642-04492-2_22
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