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By Yagang Zhang

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Extra resources for Application of Machine Learning

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2008). A survey of techniques for internet traffic classification using machine learning, Communications Surveys & Tutorials, IEEE 10(4): 56–76. NLANR PMA: Special Traces Archive (2009). , Sweldens, W. & Weiss, A. (2002). A compound model for TCP connection arrivals for LAN and WAN applications, Elsevier Science Computer Networks 40(3): 319–337. -R. -C. (2006a). Internet traffic classification for scalable QOS provision, Multimedia and Expo, 2006 IEEE International Conference on, pp. 1221–1224.

Classification Procedure Figure 1 shows the general architecture for traffic capture. Packets coming from a LAN to the Internet and vice versa are all copied to a PC, generally equipped with specialized hardware, which can either perform real-time classification or simply write to a disk a traffic trace, which is a copy of all the captured packets. In case the traffic trace is later made public, all the packets are anonymized by substituting their IP source and destination addresses and stripping the application payload.

The exhaustive search has an exponential complexity in N (number of classifier in the pool, which is 47 in our case). Hence, we cannot apply the exhaustive search algorithm on all the 47 classifiers of the pool. We then selected 5 classifiers randomly and applied both the exhaustive and the greedy algorithms on them. We repeated this 15 times and calculated the average of the resulting errors and complexities (see Table 2). Table 2 shows that our algorithm achieves a reduction in complexity that is near to the optimal case.

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