Name
Abstract | ||
|---|---|---|
The threat of cyber attacks motivates the need to monitor Internet traffic data for potentially abnormal behavior. Due to the enormous volumes of such data, statistical process monitoring tools, such as those traditionally used on data in the product manufacturing arena, are inadequate. ''Exotic'' data may indicate a potential attack; detecting such data requires a characterization of ''typical'' data. We devise some new graphical displays, including a ''skyline plot,'' that permit ready visual identification of unusual Internet traffic patterns in ''streaming'' data, and use appropriate statistical measures to help identify potential cyberattacks. These methods are illustrated on a moderate-sized data set (135,605 records) collected at George Mason University. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.csda.2005.06.017 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
internet traffic data,potential attack,abnormal behavior,unusual internet traffic pattern,statistical process monitoring tool,cyber attack,appropriate statistical measure,moderate-sized data,potential cyberattacks,george mason university,internet traffic,logarithmic transformation,computer graphic,exploratory data analysis | Skyline,Data transformation (statistics),Computer science,Visual identification,Statistical process monitoring,Recursive computation,Potentially abnormal,Statistics,Exploratory data analysis,Internet traffic | Journal |
Volume | Issue | ISSN |
50 | 12 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
1 | 0.35 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karen Kafadar | 1 | 46 | 6.32 |
| Edward J. Wegman | 2 | 36 | 7.84 |