White Papers

Dirichlet-Enhanced Spam Filtering Based on Biased Samples

Category: Security

Tags: spam, email

Overview This paper studies a setting that is motivated by the problem of filtering spam messages for many users. Each user receives messages according to an individual, unknown distribution, reflected only in the unlabeled inbox. The spam filter for a user is required to perform well with respect to this distribution. Labeled messages from publicly available sources can be utilized, but they are governed by a distinct distribution, not adequately representing most inboxes. They devise a method that minimizes a loss function with respect to a user's personal distribution based on the available biased sample. A nonparametric hierarchical Bayesian model furthermore generalizes across users by learning a common prior which is imposed on new email accounts.

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Publisher
Max-Planck-Institut für Informatik
File Format
PDF
Date Published
Jul 1, 2009
Format
White Papers
Topics
Spam - E-mail Fraud - Phishing, Network Security, Security Management

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