Abstract
We studied the objective of spam based on a financial profit truth – the cost for sending spam against anti-spam techniques should be little than the return from the negligible response from the recipients. Spammers have to leave some real contact information in the spam for the recipients to touch them easily, no matter what methods they use to fight against anti-spam techniques. In this paper, we present a method to automatically identify such contact information entities in spam, and build an online blocklist for the spam filters to classify spam, especial unsolicited commercial email (UCE).
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Symantec Spam Statistics (online) (2005), https://enterprisesecurity.symantec.com/Content/displaypdf.cfm?SSL=YES&PDFID=2059
Jason, D.M.R.: ifile: An Application of Machine Learning to E-Mail Filtering. In: Proceeding of KDD Workshop on Text Mining (1998)
Tony, M., Brendon, W.: SpamBayes: Effective Open-source, Bayesian based, Email Classification System. In: Proceeding of First Conference on Email and Anti-Spam (CEAS), Mountain View, CA (2004)
Harris, D., Donghui, W., Vladimir, N.V.: Support Vector Machines for Spam Categorization. IEEE Transaction on Neural Networks 10, 1048–1054 (1999)
Cloudmark, SpamNet (online) (2005), http://www.cloudmark.com
Rhyolite Softwrae, Distributed Checksum Clearinghouse (online) (2005), http://www.rhyolite.com/anti-sapm/doc/
SpamAssassin Public Corpus (online) (2003), http://spamassassin.org/publiccorpus
Tom, M.M.: Machine Learning. McGraw-Hill Companies, Inc., New York (1997)
Barry, L., Nathaniel, B.: A Multifaceted Approach to Spam Reduction. In: Proceeding of First Conference on Email and Anti-Spam (CEAS), Mountain View, CA (2004)
Postel, J.: On the Junk Mail Problem. RFC 706, Internet Engineering Task Force (1975)
Flavio, D.G., Jaaphenk, H., Jeroen, V.N.: Spam Filter Analysis. In: Security and Protection in Information Processing Systems (SEC 2004), France, pp. 395–410 (2004)
Vitor, R.C., Cohen, W.W.: Learning to Extract Signature and Reply Lines From Email. In: Proceeding of First Conference on Email and Anti-Spam (CEAS), Mountain View, CA (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chim, H. (2005). To Build a Blocklist Based on the Cost of Spam. In: Deng, X., Ye, Y. (eds) Internet and Network Economics. WINE 2005. Lecture Notes in Computer Science, vol 3828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11600930_51
Download citation
DOI: https://doi.org/10.1007/11600930_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30900-0
Online ISBN: 978-3-540-32293-1
eBook Packages: Computer ScienceComputer Science (R0)