Bayesian clustering for email campaign detection

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Bayesian clustering for email campaign detection
We discuss the problem of clustering elements according to the sources that have generated them. For elements that are characterized by independent binary attributes, a closedform Bayesian solution exists. We derive a solution for the case of dependent attributes that is based on a transformation of the instances into a space of independent feature functions. We derive an optimization problem that produces a mapping into a space of independent binary feature vectors; the features can reflect arbitrary dependencies in the input space. This problem setting is motivated by the application of spam filtering for email service providers. Spam traps deliver a real-time stream of messages known to be spam. If elements of the same campaign can be recognized reliably, entire spam and phishing campaigns can be contained. We present a case study that evaluates Bayesian clustering for this application.
Peter Haider, Tobias Scheffer
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Peter Haider, Tobias Scheffer
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