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A semi-supervised approach for network traffic labeling

A semi-supervised approach for network traffic labeling

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As discussed in the previous two chapters, the recent promising studies for network classification have relied on the analysis of the statistics of traffic flows and the use of machine learning (ML) methods. However, due to the high cost of manual labeling, it is hard to obtain sufficient, reliable, and up-to-date labeled data for effective IP traffic classification. This chapter discusses a novel semi-supervised approach, called SemTra, which automatically alleviates the shortage of labeled flows for ML by exploiting the advantages of both supervised and unsupervised models. In particular, SemTra involves the followings: (i) generating multi-view representations of the original data based on dimensionality reduction methods to have strong discrimination ability; (ii) incorporating the generated representations into the ensemble clustering model to provide a combined clustering output with better quality and stability; (iii) adapting the concept of self-training to iteratively utilize the few labeled data along with unlabeled within local and global viewpoints; and (iv) obtaining the final class decision by combining the decisions of mapping strategy of clusters, the local self-training and global self-training approaches. Extensive experiments were carried out to compare the effectiveness of SemTra over representative semi-supervised methods using 16 network traffic datasets. The results clearly show that SemTra is able to yield noticeable improvement in accuracy (as high as 94.96%) and stability (as high as 95.04%) in the labeling process.

Chapter Contents:

  • 9.1 Introduction
  • 9.2 The semi-supervised traffic flow labeling
  • 9.2.1 The multi-view layer
  • 9.2.2 Initial clustering analysis
  • 9.2.3 Ensemble clustering
  • 9.2.4 Local self-training
  • 9.2.5 Global self-training on meta-level features
  • 9.2.6 Function agreement and labeling
  • 9.3 Experimental evaluation
  • 9.3.1 Datasets used in experiments
  • 9.3.2 The baseline methods
  • 9.3.3 The experimental setup
  • 9.3.4 Performance metrics
  • 9.3.5 Analysis of the experimental results
  • 9.4 Conclusion

Inspec keywords: pattern classification; IP networks; telecommunication traffic; unsupervised learning; pattern clustering

Other keywords: up-to-date labeled data; ML; dimensionality reduction methods; supervised models; network classification; traffic flows; labeling process; labeled flows; global viewpoints; global self-training approaches; manual labeling; local viewpoints; network traffic labeling; cluster mapping strategy; network traffic datasets; representative semisupervised methods; effective IP traffic classification; machine learning methods; final class decision; novel semisupervised approach; unsupervised models; SemTra; local self-training; strong discrimination ability; generated representations; ensemble clustering model; multiview representation generation; statistics analysis; combined clustering output

Subjects: Computer networks and techniques; Other topics in statistics; Computer communications; Knowledge engineering techniques; Other topics in statistics; Data handling techniques

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