Previous methods of network anomaly detection have focused on defining a temporal model of what is "normal," and flagging the "abnormal" activity that does not...
Kevin M. Carter, Richard Lippmann, Stephen W. Boye...
The ubiquity of the Internet connection to desktops has been both boon to business as well as cause for concern for the security of digital assets that may be unknowingly exposed....
In this paper, we describe disparity, a tool that does parallel, scalable anomaly detection for clusters. Disparity uses basic statistical methods and scalable reduction operation...
Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other d...
Christopher Potter, Haibin Cheng, Pang-Ning Tan, S...
Anomaly detection is a promising approach to detecting intruders masquerading as valid users (called masqueraders). It creates a user profile and labels any behavior that deviates...