Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1177/1473871613488573 | INFORMATION VISUALIZATION |
Keywords | Field | DocType |
Interactive visual analytics, semantic zooming, pixel oriented, multivariate visualization, geospatial analysis, interaction design | Data science,Internet Protocol,Computer science,Computer security,Visual analytics,Cultural analytics,Artificial intelligence,Analytics,Geospatial analysis,Computer vision,Interaction design,Zoom,Semantic analytics | Journal |
Volume | Issue | ISSN |
14 | 1 | 1473-8716 |
Citations | PageRank | References |
2 | 0.39 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor Y. Chen | 1 | 42 | 3.25 |
| Ahmad M. Razip | 2 | 6 | 0.88 |
| Sungahn Ko | 3 | 87 | 10.20 |
| Cheryl Z. Qian | 4 | 2 | 0.72 |
| David S. Ebert | 5 | 2056 | 232.34 |