Visualizing "typical" and "exotic" Internet traffic data
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. (C) 2005 Elsevier B.V. All rights reserved.
graphical displays logarithmic transformation computational methods recursive computation exploratory data analysis