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A Schematic Representation of User Model Transfer for Email Virus Detection

Journal: International Journal of Advanced Networking and Applications (Vol.1, No. 06)

Publication Date:

Authors : ; ; ;

Page : 405-410

Keywords : Vulnerability; Baye’sclassification; Latent Dirichlet Allocation; per-user mixture model; global mixture model; SMTP engines;

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Abstract

Systems for learning to detect anomalous email behavior, such as worms and viruses, tend to build either per user 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. We use the Latent Dirichlet Allocation model to transition smoothly from the global prior to a particular user’s empirical model as the amount of user data grows. Preliminary results demonstratelong-term accuracy comparable to per-user models, while also showing near-ideal performance almost immediately on new users.

Last modified: 2015-12-05 20:15:00