Bayesian Clustering for Email Campaign Detection

author: Peter Haider, Institute of Computer Science, University of Potsdam
published: Aug. 26, 2009,   recorded: June 2009,   views: 3615


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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 closed-form 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.

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Reviews and comments:

Comment1 unitedbronx, January 17, 2022 at 5:38 p.m.:

Very often all attempts to conduct more or less decent email campaign fails because your mail client doesn't work properly. I had this to happen all the time.

Comment2 Barbaraaa, January 17, 2022 at 6:58 p.m.:

This problem is very well known to me. I used to have lots of trouble with my email client too. And I'd used to procrastinate changing it for long until I found that explained how to transfer *eml files to Outlook format. Thank God! I hope I won't have any issues again.

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