Title

Interactive three-dimensional visualization of network intrusion detection data for machine learning

RIS ID

138276

Publication Details

Zong, W., Chow, Y. & Susilo, W. (2020). Interactive three-dimensional visualization of network intrusion detection data for machine learning. Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, 102 292-306.

Abstract

The threat of cyber-attacks is on the rise in the digital world today. As such, effective cybersecurity solutions are becoming increasingly important for detecting and combating cyber-attacks. The use of machine learning techniques for network intrusion detection is a growing area of research, as these techniques can potentially provide a means for automating the detection of attacks and abnormal traffic patterns in real-time. However, misclassification is a common problem in machine learning for intrusion detection, and the improvement of machine learning models is hindered by a lack of insight into the reasons behind such misclassification. This paper presents an interactive method of visualizing network intrusion detection data in three-dimensions. The objective is to facilitate the understanding of network intrusion detection data using a visual representation to reflect the geometric relationship between various categories of network traffic. This interactive visual representation can potentially provide useful insight to aid the understanding of machine learning results. To demonstrate the usefulness of the proposed visualization approach, this paper presents results of experiments on commonly used network intrusion detection datasets.

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Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.future.2019.07.045