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

机译：用于机器学习的网络入侵检测数据的交互式三维可视化

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摘要

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. (C) 2019 Elsevier B.V. All rights reserved.
机译：在当今的数字世界中，网络攻击的威胁正在上升。因此，有效的网络安全解决方案对于检测和打击网络攻击变得越来越重要。使用机器学习技术进行网络入侵检测是一个不断发展的研究领域，因为这些技术可以潜在地提供一种自动实时检测攻击和异常流量模式的方法。但是，误分类是入侵检测的机器学习中的常见问题，并且由于缺乏对这种误分类背后原因的了解，阻碍了机器学习模型的改进。本文提出了一种交互式的可视化三维入侵检测数据的方法。目的是使用可视化表示法反映网络流量的各种类别之间的几何关系，以促进对网络入侵检测数据的理解。这种交互式视觉表示可以潜在地提供有用的见解，以帮助理解机器学习结果。为了证明所提出的可视化方法的有效性，本文介绍了常用网络入侵检测数据集的实验结果。 （C）2019 Elsevier B.V.保留所有权利。

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