Abstract: A framework is provided for modeling the activity surrounding user credentials and/or machine level activity on a computer network using computer event logs by viewing the logs attributed to each user as a multivariate data stream. The methodology performs well in detecting compromised user credentials at a very low false positive rate. Such a methodology may detect both users of compromised credentials by external actors and otherwise authorized users who have begun engaging in malicious activity.

Abstract: Significant and aggregate user authentication activity may be analyzed across a population of users and computers in one or more networks to differentiate between authorized users and intruders in a network, and/or to detect inappropriate behavior by otherwise authorized users. Dynamic graphs and graph models over user and computer authentication activity, including time-constrained models, may be used for the purposes of profiling and analyzing user behavior in computer networks. More specifically, an edge-based breadth first search of graphs may be used that enforces time-constraints while maintaining traditional breadth first search computational complexity equivalence.