Advertisement

Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data

  • Yoshihiro OkadaEmail author
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 4)

Abstract

This chapter treats an interactive visual analysis tool called PCTT, Parallel Coordinates Version of Time-tunnel, for multidimensional data and multi-attributes data. Especially, in this chapter, the author introduces the combinatorial use of PCTT and 2Dto2D visualization functionality for visual analytics of network data. 2Dto2D visualization functionality displays multiple lines those represent four-dimensional (four attributes) data drawn from one (2D, two attributes) plane to the other (2D, two attributes) plane in a 3D space. Network attacks like the intrusion have a certain access pattern strongly related to the four attributes of IP packet data, i.e., source IP, destination IP, source Port, and destination Port. So, 2Dto2D visualization is useful for detecting such access patterns. Although it is possible to investigate access patterns of network attacks at the attributes level of IP packets using 2Dto2D visualization functionality, statistical analysis is also necessary to find out suspicious periods of time that seem to be attacked. This is regarded as the macro level visual analytics and the former is regarded as the micro level visual analytics. In this chapter, the author also introduces such combinatorial use of PCTT for macro level to micro level visual analytics of network data as an example of multidimensional data. Furthermore, the author introduces other visual analytics example about sensor data to clarify the usefulness of PCTT.

Keywords

3D visualization Parallel Coordinates Time-tunnel Intrusion detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akaishi, M., Okada, Y.: Time-tunnel: Visual Analysis Tool for Time-series Numerical Data and Its Aspects as Multimedia Presentation Tool. In: Proc. of 8th Int. Conf. on Information Visualization (IV 2004), pp. 456–461. IEEE CS Press (2004)Google Scholar
  2. 2.
    Akaishi, M., Okada, Y.: Time-tunnel: Visual Analysis Tool for Time-series Numerical Data and Its Combinational Variation. In: Proc. of 1st Int. Conf. on Geometric Modeling, Visualization & Graphics (GMVAG 2005), Salt Lake, USA, July 21- 26 (2005)Google Scholar
  3. 3.
    Notsu, H., Okada, Y., Akaishi, M., Niijima, K.: Time-tunnel: Visual Analysis Tool for Time-series Numerical Data and Its Extension toward Par-allel Coordinates. In: Proc. of Int. Conf. on Computer Graphics, Imaging and Vision (CGIV 2005) (July 2005)Google Scholar
  4. 4.
    Inselberg, A., Dimsdale, B.: Parallel Coordinates: A Tool for Visualizing Multi-dimensional Geometry. In: Proc. IEEE Visualization 1990, pp. 361–378. IEEE CS Press (1990)Google Scholar
  5. 5.
    Okada, Y., Tanaka, Y.: IntelligentBox: A Constructive Visual Software Development System for Interactive 3D Graphic Applications. In: Proc. of Computer Animation 1995, pp. 114–125. IEEE CS Press (1995)Google Scholar
  6. 6.
    Martin, A., Ward, M.O.: High dimensional brushing for interactive explo-ration of multivariate data. In: Proc. IEEE Visualization 1995, pp. 271–278 (1995)Google Scholar
  7. 7.
    Fua, Y.-H., Ward, M.O., Rundensteiner, E.A.: Hierarchical Parallel Coor-dinates for Exploration of Large Datasets. In: Proc. IEEE Visualization 1999, pp. 43–50. IEEE CS Press (1999)Google Scholar
  8. 8.
    Hauser, H., Ledermann, F., Doleisch, H.: Angular Brushing of Extended Parallel Coordinates. In: IEEE Information Visualization (InfoVis 2002), pp. 127–130 (2002)Google Scholar
  9. 9.
    Graham, M., Kennedy, J.: Using Curves to Enhance Parallel Coordinate Visualizations. In: Proc. Information Visualization IV 2003, pp. 10–16. IEEE CS Press (2003)Google Scholar
  10. 10.
    Artero, A.O., Ferreira de Oliveira, M.C., Levkowitz, H.: Uncovering Clus-ters in Crowded Parallel Coordinates Visualizations. In: IEEE Information Visualization 2004 (InfoVis 2004), pp. 131–136 (2004)Google Scholar
  11. 11.
    Johansson, J., Cooper, M., Jern, M.: 3-Dimensional Display for Clustered Multi-Relational Parallel Coordinates. In: IEEE Information Visu-alization (InfoVis 2005), pp. 188–193 (2005)Google Scholar
  12. 12.
  13. 13.
    Lanzenberger, M., Miksch, S.: The Stardinates - Visualizing Highly Structured Data. In: Proc. of Information Visualization IV 2003, pp. 47–52. IEEE CS Press (2003)Google Scholar
  14. 14.
    Fanea, E., Carpendale, S., Isenberg, T.: An Interactive 3D Integration of Parallel Coordinates and Star Glyphs. In: IEEE Information Visualization (InfoVis 2005), pp. 149–156 (2005)Google Scholar
  15. 15.
    Tominski, C., Abello, J., Schumann, H.: 3D Axes-Based Visualizations for Time Series Data, Poster Chapter. In: IEEE Information Visualization (InfoVis 2005) (2005)Google Scholar
  16. 16.
    McPherson, J., Ma, K.-L., Krystosk, P., Bartoletti, T., Christensen, M.: Portvis: A tool for port-based detection of security events. In: ACM VizSEC 2004 Workshop, pp. 73–81 (2004)Google Scholar
  17. 17.
    Muelder, C., Ma, K.-L., Bartoletti, T.: Interactive Visualization for Network and Port Scan Detection. In: Valdes, A., Zamboni, D. (eds.) RAID 2005. LNCS, vol. 3858, pp. 265–283. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Boschetti, A., Muelder, C., Salgarelli, L., Ma, K.-L.: TVi: A Visual Querying System for Network Monitoring and Anomaly Detection. In: The 8th Int. Symp. on Visualization for Cyber Security, VizSec 2011 (2011)Google Scholar
  19. 19.
    Kintzel, C., Fuchs, J., Mansmann, F.: Monitoring Large IP Spaces with ClockView. In: The 8th Int. Symp. on Visualization for Cyber Security, VizSec 2011 (2011)Google Scholar
  20. 20.
    Best, D.M., Bohn, S., Love, D., Wynne, A., Pike, W.A.: Real-Time Visualization of Network Behaviors for Situational Awareness. In: VizSec 2010, pp. 79–90 (2010)Google Scholar
  21. 21.
    Yin, X., Yurcik, W., Treaster, M., Li, Y., Lakkaraju, K.: VisFlowConnect: NetFlow Visualizations of Link Relationships for Security Situational Awareness. In: VizSEC/DMSEC 2004, pp. 26–34 (2004)Google Scholar
  22. 22.
    Axelsson, S.: Visualization for Intrusion Detection - Hooking the Worm. Understanding Intrusion Detection Through Visualization Advances in Information Security 24, 111–127 (2006)CrossRefGoogle Scholar
  23. 23.
    Chu, M., Ingols, K., Lippmann, R., Webster, S., Boyer, S.: Visualizing Attack Graphs, Reachability, and Trust Relationships with NAVIGATOR. In: The 7th Int. Symp. on Visualization for Cyber Security, VizSec 2010, pp. 22–33 (2010)Google Scholar
  24. 24.
    Itoh, T., Takakura, H., Sawada, A., Koyamada, K.: Hierarchical Visualization of Network Intrusion Detection Data. IEEE Computer Graphics and Applications, 40–47 (March/April 2006)Google Scholar
  25. 25.
    Lau, S.: The Spinning Cube of Potential Doom. Communications of the ACM 47(6), 25–26 (2004)CrossRefGoogle Scholar
  26. 26.
    Wang, W., Lu, A.: Visualization Assisted Detection of Sybli Attacks in Wireless Networks. In: VizSEC 2006, pp. 51–60 (2006)Google Scholar
  27. 27.
    Malecot, E.L., Kohara, M., Hori, Y., Sakurai, K.: Interactively Combining 2D and 3D Visualization for Network Traffic Monitoring. In: VizSEC 2006, pp. 123–127 (2006)Google Scholar
  28. 28.
    Oberheide, J., Karir, M., Blazakis, D.: VAST: Visualizing Autonomous System Topology. In: VizSec 2006, pp. 71–79 (2006)Google Scholar
  29. 29.
    Inoue, D., Suzuki, M., Eto, M., Yoshioka, K., Nakao, K.: DAEDALUS: Novel Application of Large-Scale Darknet Monitoring for Practical Protection of Live Networks (Extended Abstract). In: Kirda, E., Jha, S., Balzarotti, D. (eds.) RAID 2009. LNCS, vol. 5758, pp. 381–382. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  30. 30.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.ICER, Kyushu University LibraryKyushu UniversityNishi-kuJapan

Personalised recommendations