Some papers on spam

I read Communications of the ACM, February 2007 Volume 50, Number 2.
"SPAM and The Ongoing Battle for the Inbox".
by Joshua Goodman, Gordon V. Cormack, and David Heckerman.

this article describe some techniques on anti-spam by reviewing some papers on references and their experience research on Microsoft
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Even as spammers and phisher try ever more sophisticated techniques to get past filters and into users mailboxes, anti-spam researchers have managed to stay several steps ahead so far.

References:
[1] Bratko, A., Cormack, G., Filipic, B., Lynam, T., and Zupan, B. Spam filtering using statistical data compression models. Journal of Machine Learning Research 7 (Dec 2006).
[2] Chellapila, K. and Simard, P. Using Machine learning to break visual human interaction proofs. In proceeding of the advances in Neural Information Processing Systems (NIPS) conference (Vancouver, Canda). MIT Press, 2005, 265-272.
[3] Chellapila, K, Simard, P., and Czerwinski, M. Computer beat humans at single character recognition in reading based human interaction proofs (HIPs). In proceeding of the second conference on Email and Anti-Spam (CEAS) (Palo Alto, CA, July 21-22, 2005).
[4] Dwork, C. and Naor, M. Pricing via processing or combatting junk mail. In proceedings of the 12th Annual International Cryptology Conference (Lecture Notes in Computer Science) (Santa Barbara, CA, Aug 16-20). Springer, 1992, 137-147.
[5] Goodman, J. and Rounthwaite, R. Stopping outgoing spam. In proceedings of the ACM conference on Electronic Commerce (EC'04) (New York, May 17-20). ACM Press, New York, 2004, 30-39.
[6] Hulten, G., Penta, A., Seshadrinathan, G., and Mishra, M. Trends in spam products and methods. In proceeding of the first conference on Email and Anti-Spam (CEAS) (Mountain View, CA, July 30-31, 2004).
[7] Kolcz, A., Chowdhury, A., and Alspector, J. The impact of feature selection on signature-driven spam detection. In proceedings of the first conference on Email and Anti-Spam (CEAS) (Mountain View, CA, July 30-31, 2004).
[8] Messaging Anti-Abuse Working Group. MAAWG Email Metrics Program, First Quater 2006 Report June 2006;http://www.maawg.org/about/FINAL_1Q2006_Metrics_Report.pdf
[9] Naor, M. Verification of a Human in the Loop or Identification via the Turing test; http://www.wisdom.weizmann.ac.id/~naor
[10] Rigoutsos, I. and Huynh, T. Chung-Kwei. A pattern discovery based system for the automatic identification of unsolicited e-mail messages. In proceedings of the first conference on Email and Anti-Spam (CEAS) (Mountain View, CA, July 30-31, 2004).
[11] Sahami, M., Dumais, S., Heckerman, D., and Horvitz, E. A Bayesian approach to filtering junk e-mail. In Learning for text categorization-Papers from the AAAI workshop. AAAI technical report WS-98-05 (Madison, WI, 1998).
[12] Yih, Y., Goodman, J., and Hulten, G. Learning at low false positive rates. In proceedings of the third conference on Email and Anti-Spam (CEAS) (Mountain View, CA, July 27-28,2006).