Advertisement

A Median Nearest Neighbors LDA for Anomaly Network Detection

  • Zyad ElkhadirEmail author
  • Khalid Chougdali
  • Mohammed Benattou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10194)

Abstract

The Linear Discriminant Analysis (LDA) is a powerful linear feature reduction technique. It often produces satisfactory results under two conditions. The first one requires that the global data structure and the local data structure must be coherent. The second concerns data classes distribution nature. It should be a Gaussian distribution. Nevertheless, in pattern recognition problems, especially network anomalies detection, these conditions are not always fulfilled. In this paper, we propose an improved LDA algorithm, the median nearest neighbors LDA (median NN-LDA), which performs well without satisfying the above two conditions. Our approach can effectively get the local structure of data by working with samples that are near to the median of every data class. The further samples will be essential for preserving the global structure of every class. Extensive experiments on two well known datasets namely KDDcup99 and NSL-KDD show that the proposed approach can achieve a promising attack identification accuracy.

Keywords

LDA median NN-LDA Network anomaly detection NSL-KDD KDDcup99 

References

  1. 1.
    Fukunaga, R.: Statistical Pattern Recognition. Academic Press, New York (1990)zbMATHGoogle Scholar
  2. 2.
    Thapngam, T., Yu, S., Zhou, W.: DDoS discrimination by linear discriminant analysis (LDA). In: 2012 International Conference on Computing, Networking and Communications (ICNC), pp. 532–536. IEEE (2012)Google Scholar
  3. 3.
    An, W., Liang, M.: A new intrusion detection method based on SVM with minimum within-class scatter. Secur. Commun. Netw. 6(9), 1064–1074 (2013)CrossRefGoogle Scholar
  4. 4.
    Subba, B., Biswas, S., Karmakar, S.: Intrusion detection systems using linear discriminant analysis and logistic regression. In: 2015 Annual IEEE India Conference (INDICON), pp. 1–6. IEEE (2015)Google Scholar
  5. 5.
    Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recogn. 34(10), 2067–2070 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Chen, L.F., Liao, H.Y.M., Ko, M.T., Lin, J.C., Yu, G.J.: A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn. 33(10), 1713–1726 (2000)CrossRefGoogle Scholar
  7. 7.
    Jolliffe, I.: Principal Component Analysis. Wiley Online Library (2002)Google Scholar
  8. 8.
    Zhang, T., Fang, B., Tang, Y.Y., Shang, Z., Xu, B.: Generalized discriminant analysis: a matrix exponential approach. IEEE Trans. Syst. Man Cybern. Part B Cybern. 40(1), 186–197 (2010)CrossRefGoogle Scholar
  9. 9.
    Ye, J., Janardan, R., Park, C.H., Park, H.: An optimization criterion for generalized discriminant analysis on under sampled problems. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 982–994 (2004)CrossRefGoogle Scholar
  10. 10.
    Wang, X., Tang, X.: Random sampling for subspace face recognition. Int. J. Comput. Vis. 70(1), 91–104 (2006)CrossRefGoogle Scholar
  11. 11.
    Liu, J., Chen, S., Tan, X.: A study on three linear discriminant analysis based methods in small sample size problem. Pattern Recogn. 41(1), 102–116 (2008)CrossRefzbMATHGoogle Scholar
  12. 12.
    Dai, D.Q., Yuen, P.C.: Regularized discriminant analysis and its application to face recognition. Pattern Recogn. 36(3), 845–847 (2003)CrossRefzbMATHGoogle Scholar
  13. 13.
    Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 4–13 (2005)CrossRefGoogle Scholar
  14. 14.
    Li, H., Jiang, T., Zhang, K.: Efficient and robust feature extraction by maximum margin criterion. IEEE Trans. Neural Netw. 17(1), 157–165 (2006)CrossRefGoogle Scholar
  15. 15.
    Ye, J., Li, Q., Xiong, H., Park, H., Janardan, R., Kumar, V.: IDR/QR: an incremental dimension reduction algorithm via QR decomposition. IEEE Trans. Knowl. Data Eng. 17(9), 1208–1222 (2005)CrossRefGoogle Scholar
  16. 16.
    Pang, S., Ozawa, S., Kasabov, N.: Incremental linear discriminant analysis for classification of data streams. IEEE Trans. Syst. Man Cybern. Part B Cybern. 35(5), 905–914 (2005)CrossRefGoogle Scholar
  17. 17.
    Kim, T.K., Wong, S.F., Stenger, B., Kittler, J., Cipolla, R.: Incremental linear discriminant analysis using sufficient spanning set approximations. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)Google Scholar
  18. 18.
    Li, X., Hu, W., Wang, H., Zhang, Z.: Linear discriminant analysis using rotational invariant L1 norm. Neurocomputing 73(13), 2571–2579 (2010)CrossRefGoogle Scholar
  19. 19.
    Wang, H., Lu, X., Hu, Z., Zheng, W.: Fisher discriminant analysis with L1-norm. IEEE Trans. Cybern. 44(6), 828–842 (2014)CrossRefGoogle Scholar
  20. 20.
    Yang, J., Zhang, D., Yang, J.Y.: Median LDA: a robust feature extraction method for face recognition. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2006, vol. 5, pp. 4208–4213. IEEE (2006)Google Scholar
  21. 21.
    Golub, G.H., Van Loan, C.F.: Matrix Computations. Johns Hopkins Studies in the Mathematical Sciences. Hopkins University Press, Baltimore (1996)zbMATHGoogle Scholar
  22. 22.
    Sugiyama, M., Idé, T., Nakajima, S., Sese, J.: Semi-supervised local fisher discriminant analysis for dimensionality reduction. Mach. Learn. 78(1–2), 35–61 (2010)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Chen, H.T., Chang, H.W., Liu, T.L.: Local discriminant embedding and its variants. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 846–853. IEEE (2005)Google Scholar
  24. 24.
    Wang, H., Chen, S., Hu, Z., Zheng, W.: Locality-preserved maximum information projection. IEEE Trans. Neural Netw. 19(4), 571–585 (2008)CrossRefGoogle Scholar
  25. 25.
  26. 26.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zyad Elkhadir
    • 1
    Email author
  • Khalid Chougdali
    • 2
  • Mohammed Benattou
    • 1
  1. 1.LASTID LaboratoryIbn Tofail UniversityKenitraMorocco
  2. 2.GEST Research Group, National School of Applied Sciences (ENSA)Ibn Tofail UniversityKenitraMorocco

Personalised recommendations