Anti-Virus White Papers
User Model Transfer for Email Virus Detection
Overview 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 finegrained 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.
| Publisher | University of California | File Format | |
|---|---|---|---|
| Date Published | May 2006 | Downloads | 1 |
| Format | White Papers | ||
| Topics | |||
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