A 3D Approach for the Visualization of Network Intrusion Detection Data
A 3D Approach for the Visualization of Network Intrusion Detection Data
复制标题
网络入侵检测数据可视化的 3D 方法
DOI：
10.1109/cw.2018.00064
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
W. Susilo
中科院分区：
文献类型：
--
作者：
W. Zong;Yang;W. Susilo
随着网络攻击威胁的日益严重，机器学习技术在网络入侵检测领域得到了广泛的研究。这种技术可以潜在地提供用于实时自动检测攻击和异常流量模式的手段。然而，误分类是入侵检测机器学习技术中的一个常见问题，并且缺乏对为什么会发生这种误分类的深入了解阻碍了机器学习模型的改进。提出了一种网络入侵检测数据的三维可视化方法。这样做的目的是为了便于理解网络入侵检测数据集使用一个可视化的表示，以反映各种类别的网络流量之间的几何关系。这可能会提供有用的见解，以帮助设计机器学习技术。本文通过对常用网络入侵检测数据集的实验结果，证明了所提出的三维可视化方法的实用性。
With the increasing threat of cyber attacks, machine learning techniques have been researched extensively in the area of network intrusion detection. Such techniques can potentially provide a means for the real-time automated detection of attacks and abnormal traffic patterns. However, misclassification is a common problem in machine learning techniques for intrusion detection, and a lack of insight into why such misclassification occurs impedes the improvement of machine learning models. This paper presents an approach to visualizing network intrusion detection data in 3D. The purpose of this is to facilitate the understanding of network intrusion detection datasets using a visual representation to reflect the geometric relationship between various categories of network traffic. This can potentially provide useful insight to aid the design of machine learning techniques. This paper demonstrates the usefulness of the proposed 3D visualization approach by presenting results of experiments on commonly used network intrusion detection datasets.