Name
Abstract | ||
|---|---|---|
Many researchers have applied statistical analy- sis techniques to email for classification purposes, such as identifying spam messages. Such ap- proaches can be highly eective, however many examine incoming email exclusively — which does not provide detailed information about an individual user's behavior. Only by analyzing outgoing messages can a user's behavior be as- certained. Our contributions are: the use of em- pirical analysis to select an optimum, novel col- lection of behavioral features of a user's email trac that enables the rapid detection of abnor- mal email activity; and a demonstration of the eectiveness of outgoing email analysis using an application that detects worm propagation. |
Year | Venue | Keywords |
|---|---|---|
2005 | CEAS | col |
Field | DocType | Citations |
World Wide Web,Computer science,Email classification,Statistical analysis | Conference | 24 |
PageRank | References | Authors |
1.48 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steve Martin | 1 | 37 | 3.49 |
| Blaine Nelson | 2 | 1127 | 58.95 |
| Anil Sewani | 3 | 33 | 2.85 |
| Karl Chen | 4 | 135 | 6.30 |
| D. Joseph | 5 | 5463 | 492.96 |