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Classifying peer-to-peer applications using imbalanced concept-adapting very fast decision tree on IP data stream

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Abstract

Peer-to-Peer (P2P) applications generate streaming data in large volumes, where new communities of peers regularly attend and existing communities of peers regularly leave, requiring the classification techniques to consider concept drift, and update the model incrementally. Concept-adapting Very Fast Decision Tree (CVFDT) is one of the well-known streaming data mining techniques that can be applied to P2P traffic. However, we observe that P2P traffic data is class imbalanced, namely, only about 30 % of examples can be labeled as “P2P”, biasing the trained models (e.g. decision tree) towards the majority class (i.e. “NonP2P”). In this paper, we propose a new technique, the imbalanced CVFDT (iCVFDT), by integrating the CVFDT with an efficient resampling technique to address the issue of the class imbalanced data. The iCVFDT classification technique enjoys the advantages of CVFDT (such as stability), and at the same time, is not sensitive to imbalanced data. We captured the Internet traffic at a main gateway and prepared a real data stream with 3.5 million examples to which the iCVFDT classification technique was applied. The experimental results demonstrate a significant improvement in the performance of the iCVFDT compared to that of the CVFDT.

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Notes

  1. Recently, streaming video grows at a fast rate and accounts for more traffic on the Internet than P2P traffic. However, it is not that P2P traffic is declining. It just grows at a slower rate compared to other types of video traffic.

  2. http://www.iana.org/

  3. “length” denotes the length field after “>” in the Table 1.

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Acknowledgments

This work was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), and Fundamental Research Funds for the Central Universities and the National Natural Science Foundation of China under Grants 61103119 and 60970067.

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Authors and Affiliations

  1. IBM SPSS Statistics Research, IBM China Development Lab, Xi’an, 710065, China

    Weicai Zhong

  2. University of Ottawa, 55 Laurier Ave., Ottawa, ON, K1N 6N5, Canada

    Bijan Raahemi

  3. Xidian University, No.2 South Taibai Road, Xi’an, 710071, China

    Jing Liu

Authors
  1. Weicai Zhong
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  2. Bijan Raahemi
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  3. Jing Liu
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Corresponding author

Correspondence to Bijan Raahemi.

Appendix

Appendix

A.1The CVFDTGrow procedure

figure f

A.2 The ForgetExample procedure

figure g

A.3 The CheckSplitValidity procedure

figure h

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Zhong, W., Raahemi, B. & Liu, J. Classifying peer-to-peer applications using imbalanced concept-adapting very fast decision tree on IP data stream. Peer-to-Peer Netw. Appl. 6, 233–246 (2013). https://doi.org/10.1007/s12083-012-0147-5

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  • DOI: https://doi.org/10.1007/s12083-012-0147-5

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