The Email Mining Toolkit (EMT) is a data mining system that computes behavior profiles or models of user email accounts. These models may be used for a variety of forensic analyses and detection tasks. In this paper we focus on the application of these models to detect the early onset of a viral propagation without "contentbased" (or signature-based) analysis in common use in virus scanners. We present several experiments using real email from 15 users with injected simulated viral emails and describe how the combination of different behavior models improves overall detection rates. The performance results vary depending upon parameter settings, approaching 99% true positive(TP) (percentage of viral emails caught) in general cases and with 0.38% false positive(FP) (percentage of emails with attachments that are mislabeled as viral). The models used for this study are based upon volume and velocity statistics of a user’s email rate and an analysis of the user’s (social) cliques revealed in their email behavior. We show by way of simulation that virus propagations are detectable since viruses may emit emails at rates different than human behavior suggests is normal, and email is directed to groups of recipients that violates the user’s typical communication with their social groups.