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: 2010-05-03
Authors : M.Sreedhar Reddy; Prof.Manoj Alimilla; Viswanath Raghava.P;
Page : 405-410
Keywords : Vulnerability; Baye’sclassification; Latent Dirichlet Allocation; per-user mixture model; global mixture model; SMTP engines;
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.
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