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A Four-Stage Hybrid Feature Subset Selection Approach for Network Traffic Classification Based on Full Coverage

  • Jingbo Xia
  • Jian Shen
  • Yaoxiang Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11342)

Abstract

There is significant interest in network management and security to classify traffic flows. As the essential step for machine learning based traffic classification, feature subset selection is often used to realize dimension reduction and redundant information decrease. A four-stage hybrid feature subset selection method is proposed to improve the classification performance of hybrid methods at low evaluation consumption. The proposed algorithm is designed to dispose features in the level of block and evaluate every feature even the remaining ones which cannot provide much information by themselves to use the interactions among all of them. Additionally, a wrapper-based selection is designed in the last stage to further remove the redundant features. The performances are examined by two groups of experiments. Our theoretical analysis and experimental observations reveal that the proposed method selects feature subset with improved classification performance on every index while depleting fewer evaluations. Moreover, the evaluation consumption can keep at a low and stable level with different size of block.

Keywords

Full coverage Machine learning Hybrid feature subset selection Network traffic classification Network management 

Notes

Acknowledgements

The authors gratefully acknowledge the financial support from Natural Science Foundation of Zhangzhou, Fujian (Project No. ZZ2018J22).

References

  1. 1.
    Khayari, R.E.A., Sadre, R,, Haverkort, B.R.: A validation of the pseudo self-similar traffic model. In: International Conference on Dependable Systems and Networks, pp. 727–734. IEEE Computer Society (2002)Google Scholar
  2. 2.
    Liu, Z., Wang, R., Tao, M., et al.: A class-oriented feature selection approach for multi-class imbalanced network traffic datasets based on local and global metrics fusion. Neurocomputing 168(C), 365–381 (2015)CrossRefGoogle Scholar
  3. 3.
    Nie, F., Huang, H., Cai, X., et al.: Efficient and robust feature selection via joint ℓ2,1-norms minimization. In: International Conference on Neural Information Processing Systems, pp. 1813–1821. Curran Associates Inc (2010)Google Scholar
  4. 4.
    Nie, F., Xu, D., Tsang, I.W., et al.: Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction. IEEE Trans. Image Process. 19(7), 1921–1932 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang, R., Nie, F., Hong, R., et al.: Fast and orthogonal locality preserving projections for dimensionality reduction. IEEE Trans. Image Process. PP(99), 1-1 (2017)MathSciNetGoogle Scholar
  6. 6.
    Xie, J., Wang, C.: Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. Int. J. 38(5), 5809–5815 (2011)CrossRefGoogle Scholar
  7. 7.
    Peng, Y., Wu, Z., Jiang, J.: A novel feature selection approach for biomedical data classification. J. Biomed. Inform. 43(1), 15–23 (2010)CrossRefGoogle Scholar
  8. 8.
    Zhang, L.X,, Wang, J.X., Zhao, Y.N., et al.: A novel hybrid feature selection algorithm: using ReliefF estimation for GA-wrapper search. In: International Conference on Machine Learning and Cybernetics, vol. 1, pp. 380–384. IEEE (2004)Google Scholar
  9. 9.
    Bonilla-Huerta, E., Duval, B., Hernández, J.C.H., Hao, J.-K., Morales-Caporal, R.: Hybrid filter-wrapper with a specialized random multi-parent crossover operator for gene selection and classification problems. In: Huang, D.-S., Gan, Y., Premaratne, P., Han, K. (eds.) ICIC 2011. LNCS, vol. 6840, pp. 453–461. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-24553-4_60CrossRefGoogle Scholar
  10. 10.
    Guyon, I., Elisseeff, A., et al.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(6), 1157–1182 (2003)zbMATHGoogle Scholar
  11. 11.
    Vieira, S.M., Sousa, J.M.C., Kaymak, U.: Fuzzy criteria for feature selection. Fuzzy Sets Syst. 189(1), 1–18 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 359–366. Morgan Kaufmann Publishers Inc (2000)Google Scholar
  13. 13.
    Hsu, H.H., Hsieh, C.W., Lu, M.D.: Hybrid feature selection by combining filters and wrappers. Expert Syst. Appl. 38(7), 8144–8150 (2011)CrossRefGoogle Scholar
  14. 14.
    Bermejo, P., Ossa, L.D.L., Gámez, J.A., et al.: Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking. Knowl. Based Syst. 25(1), 35–44 (2012)CrossRefGoogle Scholar
  15. 15.
    Wald, R., Khoshgoftaar, T.M., Napolitano, A.: Stability of filter- and wrapper-based feature subset selection. In: IEEE International Conference on TOOLS with Artificial Intelligence, pp. 374–380. IEEE (2014)Google Scholar
  16. 16.
    Guyon, I., Gunn, S., Nikravesh, M., et al. (eds.): Feature Extraction: Foundations and Applications. Studies in Fuzziness and Soft Computing. Springer, New York (2005).  https://doi.org/10.1007/978-3-540-35488-8CrossRefGoogle Scholar
  17. 17.
    Shen, H., Wang, B.: An effective method for synthesizing multiple-pattern linear arrays with a reduced number of antenna elements. IEEE Trans. Antennas Propag. PP(99), 1 (2017)Google Scholar
  18. 18.
    Shen, J., Xia, J., Zhang, X., et al.: Sliding block based hybrid feature subset selection in network traffic. IEEE Access 5(99), 18179–18186 (2017)CrossRefGoogle Scholar
  19. 19.
    Shen, J., Xia, J., Dong, S., et al.: Universal feature extraction for traffic identification of the target category. PLoS ONE 11(11), e0165993 (2016)CrossRefGoogle Scholar
  20. 20.
    Fialho, A.S., et al.: Predicting outcomes of septic shock patients using feature selection based on soft computing techniques. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. CCIS, vol. 81, pp. 65–74. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14058-7_7CrossRefGoogle Scholar
  21. 21.
    Ruiz, R., Riquelme, J.C., Aguilar-Ruiz, J.S.: Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recogn. 39(12), 2383–2392 (2006)CrossRefGoogle Scholar
  22. 22.
    Bermejo, P., Gamez, J.A., Puerta, J.M.: Incremental Wrapper-based subset selection with replacement: an advantageous alternative to sequential forward selection. In: IEEE Symposium on Computational Intelligence and Data Mining, 2009 (CIDM 2009), pp. 367–374. IEEE (2009)Google Scholar
  23. 23.
    Kohavi, R., John, G.H.: Wrappers for Feature Subset Selection. Elsevier, Amsterdam (1997)CrossRefGoogle Scholar
  24. 24.
    Friedman, J., Hastie, T., et al.: The Elements of Statistical Learning, vol. 27, no. 2, pp. 83–85. Springer, Heidelberg (2009)Google Scholar
  25. 25.
    Song, Q., Ni, J., Wang, G.: A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans. Knowl. Data Eng. 25(1), 1–14 (2012)CrossRefGoogle Scholar
  26. 26.
    Quinlan, J.R.: C4. 5: Programs for Machine Learning. Morgan Kaufmann, Los Altos (1992)Google Scholar
  27. 27.
    Moore, A.W.: Dataset. http://www.cl.cam.ac.uk/research/srg/netos/nprobe/data/papers. Accessed Aug 2013
  28. 28.
    Croft, B., Metzler, D., Search, S.T.: Engines—information retrieval in practice. Comput. J. 54(5), 831–832 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Tan Kah CollegeXiamen UniversityZhangzhouChina
  2. 2.Unit 95655 of PLAChengduChina

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