Abstract
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.
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Notes
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Packet CAPture.
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Comma-Separated Values.
References
Auld, T., Moore, A.W., Gull, S.F.: Bayesian neural networks for internet traffic classification. IEEE Trans. Neural Netw. 18(1), 223–239 (2007). https://doi.org/10.1109/TNN.2006.883010
Card, S.K., Mackinlay, J.D., Shneiderman, B. (eds.): Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers Inc., San Francisco (1999)
Cisco and/or its affiliates: Cisco visual networking index: forecast and methodology, 2016–2021. Technical report C11-481360-01. Cisco Systems, Inc. (2017). https://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-index-vni/index.html
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)
flowRecorder: A network traffic flow feature measurement tool (2018). https://github.com/drnpkr/flowRecorder
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). https://doi.org/10.1109/TVCG.2011.166
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). https://doi.org/10.1109/COMST.2015.2450538
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). https://doi.org/10.1145/1544012.1544023
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). https://doi.org/10.1109/INFCOM.2005.1498482
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). https://doi.org/10.1109/JSAC.2006.877215
Liu, S., Cui, W., Wu, Y., Liu, M.: A survey on information visualization: recent advances and challenges. Vis. Comput. 30(12), 1373–1393 (2014). https://doi.org/10.1007/s00371-013-0892-3
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)
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). https://doi.org/10.1109/SURV.2008.080406
Pekár, A., Chovanec, M., Vokorokos, L., Chovancová, E., Feciľak, P., Michalko, M.: Adaptive aggregation of flow records. Comput. Inform. 37(1), 142–164 (2018). https://doi.org/10.4149/cai_2018_1_142
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). https://doi.org/10.1109/TVCG.2011.144
Valenti, S., Rossi, D., Dainotti, A., Pescapé, A., Finamore, A., Mellia, M.: Reviewing traffic classification, pp. 123–147. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-36784-7_6
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). https://doi.org/10.1109/INES.2012.6249860
Acknowledgement
A. Pekar and W. Seah are supported by VUW’s Huawei NZ Research Programme, Software-Defined Green Internet of Things (project #E2881).
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Pekar, A., Ruan, M.B.H., Seah, W.K.G. (2020). Angular Histogram-Based Visualisation of Network Traffic Flow Measurement Data. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_30
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