Multi-aspect visual analytics on large-scale high-dimensional cyber security data. (January 2015)

• Record Type: Journal Article
• Authors: Chen, Victor Y; Razip, Ahmad M; Ko, Sungahn; Qian, Cheryl Z; Ebert, David S
• Other Names: Cook Kristin guest-editor.; Grinstein Georges guest-editor.; Whiting Mark guest-editor.
• Is Part Of: Information visualization. Volume 14:Number 1(2015:Jan.)
• Page Start: 62
• Page End: 75
• Publication Date: 2015-01
• Subjects: Interactive visual analytics -- semantic zooming -- pixel oriented -- multivariate visualization -- geospatial analysis -- interaction design; Information visualization -- Periodicals; 006.605
• DOI: 10.1177/1473871613488573
• Languages: English
• ISSNs: 1473-8716

Abstract

In this article, we present a visual analytics system, SemanticPrism, which aims to analyze large-scale high-dimensional cyber security datasets containing logs of a million computers. SemanticPrism visualizes the data from three different perspectives: spatiotemporal distribution, overall temporal trends, and pixel-based IP (Internet Protocol) address blocks. With each perspective, we use semantic zooming to present more detailed information. The interlinked visualizations and multiple levels of detail allow us to detect unexpected changes taking place in different dimensions of the data and to identify potential anomalies in the network. After comparing our approach to other submissions, we outline potential paths for future improvement.