| Publisher | Avaya | ||
|---|---|---|---|
| Format | 1.2MB PDF | Date added | 01 Feb 2007 |
| Topics | Application Servers, Email | ||
| Downloads | 34 | ||
Systems for learning to detect anomalous email behavior, such as worms and viruses, tend to build either peruser models or a single global model. Global models leverage a larger training corpus but often model individual users poorly. Per-user models capture fine-grained behaviors but can take a long time to accumulate sufficient training data. Approaches that combine global and per-user information have the potential to address these limitations. The Latent Dirichlet Allocation model is used to transition smoothly from the global prior to a particular user's empirical model as the amount of user data grows. Preliminary results demonstrate long-term accuracy comparable to per-user models, while also showing near-ideal performance almost immediately on new users.
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