Angular Histogram-Based Visualisation of Network Traffic Flow Measurement Data

  • Adrian PekarEmail author
  • Mona B. H. Ruan
  • Winston K. G. Seah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)


Knowledge of the traffic that is being carried within a network is critical for ensuring the network’s smooth operation, and network traffic measurement has provided an effective means to achieve this. However, network traffic volume has substantially increased over the last decades. Combine that with the traffic heterogeneity from a diverse range of new, connected devices, we have reached a point where the response to any outage or anomalous event is simply beyond human ability. Network information visualisation is a useful tool to help network administrators deal with this problem. While this approach is not new, traditional approaches do not scale well with the increasing volume and heterogeneity of network traffic. In this paper, we propose the application of angular histogram visualisation to provide an information-rich overview of large network traffic data sets to improve the interpretation and understanding of network traffic flow measurement data. We evaluate our approach experimentally using live network traffic to demonstrate its efficacy and provide suggestions on how it can be further improved.



A. Pekar and W. Seah are supported by VUW’s Huawei NZ Research Programme, Software-Defined Green Internet of Things (project #E2881).


  1. 1.
    Auld, T., Moore, A.W., Gull, S.F.: Bayesian neural networks for internet traffic classification. IEEE Trans. Neural Netw. 18(1), 223–239 (2007). Scholar
  2. 2.
    Card, S.K., Mackinlay, J.D., Shneiderman, B. (eds.): Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  3. 3.
    Cisco and/or its affiliates: Cisco visual networking index: forecast and methodology, 2016–2021. Technical report C11-481360-01. Cisco Systems, Inc. (2017).
  4. 4.
    Engel, D., Hagen, H., Hamann, B., Rosenbaum, R.: Structural decomposition trees: semantic and practical implications. In: Csurka, G., Kraus, M., Laramee, R.S., Richard, P., Braz, J. (eds.) Computer Vision, Imaging and Computer Graphics. Theory and Application, pp. 193–208. Springer, Berlin (2013)CrossRefGoogle Scholar
  5. 5.
    flowRecorder: A network traffic flow feature measurement tool (2018).
  6. 6.
    Geng, Z., Peng, Z., Laramee, R.S., Roberts, J.C., Walker, R.: Angular histograms: frequency-based visualizations for large, high dimensional data. IEEE Trans. Vis. Comput. Graph. 17(12), 2572–2580 (2011). Scholar
  7. 7.
    Guimaraes, V.T., Freitas, C.M.D.S., Sadre, R., Tarouco, L.M.R., Granville, L.Z.: A survey on information visualization for network and service management. IEEE Commun. Surv. Tutor. 18(1), 285–323 (2016). Scholar
  8. 8.
    Kim, H., Claffy, K., Fomenkov, M., Barman, D., Faloutsos, M., Lee, K.: Internet traffic classification demystified: myths, caveats, and the best practices. In: Proceedings of the 4th ACM Conference on Emerging Network Experiment and Technology (CoNEXT), Madrid, Spain, pp. 11:1–11:12 (2008).
  9. 9.
    Kim, S.S., Reddy, A.L.N.: A study of analyzing network traffic as images in real-time. In: Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, pp. 2056–2067 (2005).
  10. 10.
    Kim, S.S., Reddy, A.L.N.: Image-based anomaly detection technique: algorithm, implementation and effectiveness. IEEE J. Sel. Areas Commun. 24(10), 1942–1954 (2006). Scholar
  11. 11.
    Liu, S., Cui, W., Wu, Y., Liu, M.: A survey on information visualization: recent advances and challenges. Vis. Comput. 30(12), 1373–1393 (2014). Scholar
  12. 12.
    Moore, A.W., Zuev, D., Crogan, M.L.: Discriminators for use in flow-based classification. Technical report RR-05-13, Queen Mary University of London (2005)Google Scholar
  13. 13.
    Nguyen, T.T.T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surv. Tutor. 10(4), 56–76 (2008). Scholar
  14. 14.
    Pekár, A., Chovanec, M., Vokorokos, L., Chovancová, E., Feciľak, P., Michalko, M.: Adaptive aggregation of flow records. Comput. Inform. 37(1), 142–164 (2018). Scholar
  15. 15.
    Shiravi, H., Shiravi, A., Ghorbani, A.A.: A survey of visualization systems for network security. IEEE Trans. Vis. Comput. Graphi. 18(8), 1313–1329 (2012). Scholar
  16. 16.
    Valenti, S., Rossi, D., Dainotti, A., Pescapé, A., Finamore, A., Mellia, M.: Reviewing traffic classification, pp. 123–147. Springer, Berlin (2013).
  17. 17.
    Vokorokos, L., Pekar, A., Adam, N.: Data preprocessing for efficient evaluation of network traffic parameters. In: Proceedings of the 16th IEEE International Conference on Intelligent Engineering Systems, INES, pp. 363–367 (2012).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Adrian Pekar
    • 1
    Email author
  • Mona B. H. Ruan
    • 1
  • Winston K. G. Seah
    • 1
  1. 1.Victoria University of WellingtonWellingtonNew Zealand

Personalised recommendations