In this short paper, we propose the split-diffuse (SD) algorithm that takes the output of an existing word embedding algorithm, and distributes the data points uniformly across the visualization space. The result improves the perceivability and the interactability by the human. We apply the SD algorithm to analyze the user behavior through access logs within the cyber security domain. The result, named the topic grids, is a set of grids on various topics generated from the logs. On the same set of grids, different behavioral metrics can be shown on different targets over different periods of time, to provide visualization and interaction to the human experts. Analysis, investigation, and other types of interaction can be performed on the topic grids more efficiently than on the output of existing dimension reduction methods. In addition to the cyber security domain, the topic grids can be further applied to other domains like e-commerce, credit card transaction, customer service to analyze the behavior in a large scale.

我们提出了一个名为 Split-Diffuse (SD) 算法的工具能够均匀地在数据可视化空间中分配词嵌入算法的输出数据，从而改善用户感知和交互，将其应用于网络安全领域中用户行为日志分析得到了名为话题网格的结果。该话题网格可以展示不同指标的行为变化在不同的目标上以及不同时间段内的可视化和交互，可比之前的降维算法更高效地进行分析、研究和其他交互操作。除了网络安全领域，话题网格还可以应用于电子商务、信用卡交易、客户服务等其他领域，以大规模分析行为。

巨量行为数据交互