Manifold learning visualization of network traffic data
Manifold learning visualization of network traffic data
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DOI：
10.1145/1080173.1080182
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发表时间：
2005-08
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影响因子：
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通讯作者：
Neal Patwari;A. Hero;Adam Pacholski
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文献类型：
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作者：
Neal Patwari;A. Hero;Adam Pacholski
当网络上出现流量异常或入侵企图时，我们预计网络流量的分布将会改变。随时间、跨空间（在网络中的各个路由器处）、针对源端口和目的端口、IP地址或自治系统编号监测网络的变化，是异常检测的一个重要部分。我们提出了一种基于流形学习（ML）的工具，用于对大量数据集进行可视化，该工具强调数据集中存在的异常小或大的相关性。我们应用该工具来展示由NetFlow在阿比林骨干网络上记录的异常流量。此外，我们还提出了一个基于Java的在线图形用户界面（GUI），它允许对可视化方法的使用进行交互式演示。
When traffic anomalies or intrusion attempts occur on the network, we expect that the distribution of network traffic will change. Monitoring the network for changes over time, across space (at various routers in the network), over source and destination ports, IP addresses, or AS numbers, is an important part of anomaly detection. We present a manifold learning (ML)-based tool for the visualization of large sets of data which emphasizes the unusually small or large correlations that exist within the data set. We apply the tool to display anomalous traffic recorded by NetFlow on the Abilene backbone network. Furthermore, we present an online Java-based GUI which allows interactive demonstration of the use of the visualization method.