Advertisement

Computing

, Volume 97, Issue 4, pp 425–437 | Cite as

Using arced axes in parallel coordinates geometry for high dimensional BigData visual analytics in cloud computing

  • Mao Lin HuangEmail author
  • Liang Fu LuEmail author
  • Xuyun Zhang
Article

Abstract

With the rapid growth of data in size and complexity, that are available on shared cloud computing platform, the threat of malicious activities and computer crimes has increased accordingly. Thus, investigating efficient data visualization techniques for visual analytics of such big data and visual intrusion detection over data intensive cloud computing is urgently required. In this paper, we first propose a new parallel coordinates visualization method that uses arced-axis for high-dimensional data representation. This new geometrical scheme can be efficiently used to identify the main features of network attacks by displaying recognizable visual patterns. In addition, with the aim of visualizing the clear and detailed structure of the dataset according to the contribution of each attribute, we propose a meaningful layout for the new method based on singular value decomposition algorithm, which possesses statistical property and can overcome the curse of dimensionality. Finally, we design a prototype system for network scan detection, which is based on our visualization approach. The experiments have shown that our approach is effective in visualizing multivariate datasets and detecting attacks from a variety of networking patterns, such as the features of DDoS attacks.

Keywords

Multivariate data visualization High-dimensional data visualization Parallel coordinate geometry Arced-axis Network security Network intrusion detection 

Mathematics Subject Classification

14Q15 

References

  1. 1.
    Zhang X, Liu C, Nepal S, Pandey S, Chen J (2013) A privacy leakage upper-bound constraint based approach for cost-effective privacy preserving of intermediate datasets in cloud. IEEE Trans Parallel Distrib Syst 24(6):1192–1202CrossRefGoogle Scholar
  2. 2.
    Zhang X, Yang LT, Liu C, Chen J (2014) A scalable two-phase top-down specialization approach for data anonymization using mapreduce on cloud. IEEE Trans Parallel Distrib Syst 25(2):363–373Google Scholar
  3. 3.
    Zhang X, Liu C, Nepal S, Chen J (2013) An efficient quasi-identifier index based approach for privacy preservation over incremental data sets on cloud. J Comput Syst Sci 79(5):542–555CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Claessen JH, van Wijk JJ (2011) Flexible linked axes for multivariate data visualization. IEEE Trans Vis Comput Graph 17(12):2310–2316CrossRefGoogle Scholar
  5. 5.
    Inselberg A (1985) The plane with parallel coordinates. Vis Comput 1(2):69–91CrossRefzbMATHGoogle Scholar
  6. 6.
    Wegman E (1990) Hyperdimensional data analysis using parallel coordinates. J Am Stat Assoc 85(411):664–675CrossRefGoogle Scholar
  7. 7.
    Dasgupta A, Kosara R (2010) Pargnostics: screen-space metrics for parallel coordinates. IEEE Trans Vis Comput Graph 16(6):1017–1026CrossRefGoogle Scholar
  8. 8.
    Huh M-H, Park DY (2008) Enhancing parallel coordinate plots. J Korean Stat Soc 37(2):129–133CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Zhou H, Yuan X, Qu H, Cui W, Chen B (2008) Visual clustering in parallel coordinates. Comput Graph Forum 27(3):1047–1054CrossRefGoogle Scholar
  10. 10.
    Zhou H, Cui W, Qu H, Wu Y, Yuan X, Zhuo W (2009) Splatting the lines in parallel coordinates. Comput Graph Forum 28(3):759–766CrossRefGoogle Scholar
  11. 11.
    Dang TN, Wilkinson L (2010) A stacking graphic elements to avoid over-plotting. IEEE Trans Vis Comput Graph 16(6):1044–1052CrossRefGoogle Scholar
  12. 12.
    Artero AO, de Oliveira MCF, Levkowitz H (2004) Uncovering clusters in crowded parallel coordinates visualizations. In: Proceedings of IEEE symposium on information visualization, INFOVIS 2004, pp 81–88Google Scholar
  13. 13.
    Yuan X, Guo P, Xiao H, Zhou H, Qu H (2009) Scattering points in parallel coordinates. IEEE Trans Vis Comput Graph 15(6):1001–1008CrossRefGoogle Scholar
  14. 14.
    Peng W, Ward MO, Rundensteiner EA (2004) Clutter reduction in multi-dimensional data visualization using dimension reordering. In: Proceedings of IEEE symposium on information visualization, INFOVIS 2004, pp 89–96Google Scholar
  15. 15.
    Artero AO, de Oliveira MCF, Levkowitz H (2006) Enhanced high dimensional data visualization through dimension reduction and attribute arrangement. In: Proceedings of The tenth international conference on information visualization, IV 2006, pp 707–712Google Scholar
  16. 16.
    Ferdosi BJ, Roerdink JB (2011) Visualizing high-dimensional structures by dimension ordering and filtering using subspace analysis. Comput Graph Forum 30(3):1121–1130CrossRefGoogle Scholar
  17. 17.
    Claessen JH, Van Wijk JJ (2011) Flexible linked axes for multivariate data visualization. IEEE Trans Vis Comput Graph 17(12):2310–2316CrossRefGoogle Scholar
  18. 18.
    Tominski C, Abello J, Schumann H (2004 ) Axes-based visualizations with radial layouts. In: Proceedings of the ACM symposium on applied, computing, pp 1242–1247Google Scholar
  19. 19.
    Hauser H, Ledermann F, Doleisch H (2002) Angular brushing of extended parallel coordinates. In: Proceedings of IEEE symposium on information visualization, INFOVIS 2002, pp 127–130Google Scholar
  20. 20.
    Dimsdale B (1984) Conic transformations and projectivities. Technical Report, 6320-2753Google Scholar
  21. 21.
    Golub G, Van Loan C (1996) Matrix computations, vol 3. Johns Hopkins University Press, BaltimoreGoogle Scholar
  22. 22.
    Simek K (2003) Properties of singular value decomposition based dynamical model of gene expression data. Int J Appl Math Comput Sci 13(3):337–346zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  1. 1.School of Computer SoftwareTianjin UniversityTianjinPeople’s Republic of China
  2. 2.Faculty of Engineering and ITUniversity of TechnologySydneyAustralia
  3. 3.Mathematics DepartmentTianjin UniversityTianjinPeople’s Republic of China

Personalised recommendations