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Big network traffic data visualization

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Abstract

Visualization is an important tool for capturing the network activities. Effective visualization allows people to gain insights into the data information and discovery of communication patterns of network flows. Such information may be difficult for human to perceive its relationships due to its numeric nature such as time, packet size, inter-packet time, and many other statistical features. Many existing work fail to provide an effective visualization method for big network traffic data. This work proposes a novel and effective method for visualizing network traffic data with statistical features of high dimensions. We combine Principal Component Analysis (PCA) and Mutidimensional Scaling (MDS) to effectively reduce dimensionality and use colormap for enhance visual quality for human beings. We obtain high quality images on a real-world network traffic dataset named ‘ISP’. Comparing with the popular t-SNE method, our visualization method is more flexible and scalable for plotting network traffic data which may require to preserve multi-dimensional information and relationship. Our plots also demonstrate the capability of handling a large amount of data. Using our method, the readers will be able to visualize their network traffic data as an alternative method of t-SNE.

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Authors and Affiliations

  1. School of Information Technology, Deakin University, Geelong, VIC, 3220, Australia

    Zichan Ruan, Yuantian Miao & Lei Pan

  2. Digital Research, Innovation Capability Platform, Swinburne University of Technology, John Street, Hawthorn, Victoria, 3122, Australia

    Yang Xiang

  3. School of Software and Electrical Engineering, Swinburne University of Technology, John Street, Hawthorn, Victoria, 3122, Australia

    Jun Zhang

Authors
  1. Zichan Ruan
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  2. Yuantian Miao
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  3. Lei Pan
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  4. Yang Xiang
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  5. Jun Zhang
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Correspondence to Zichan Ruan.

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Ruan, Z., Miao, Y., Pan, L. et al. Big network traffic data visualization. Multimed Tools Appl 77, 11459–11487 (2018). https://doi.org/10.1007/s11042-017-5495-y

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  • DOI: https://doi.org/10.1007/s11042-017-5495-y

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