C

Bibliography in very= rough chronological order. T= aken from = Tom Fawcett’s paper (KDD Explorations ’03), The Penny Bl= ack Project, = Parameterization of Naïve Bayes for Spam Filters, by Tr= evor Stone, and An Economic Solution to the Spam Prob= lem by Loder et al.

C. Dwork and M. Naor, "Prici= ng via Processing or Combatting Junk Mail",
Lecture Notes in Computer Science 740 (Proceedings of CRYPTO'92)}, 1= 993, pp. 137--147.

W. Cohen, Learning Rules that classify Email, Adva= nces in Inductive Logic Programming 1996. http://citeseer.= ist.psu.edu/cohen96learning.html

Moni Naor, “Verificat= ion of a human in the loop or Identification via the Turing Test”,= unpublished manuscript.

M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A Bayesian Appr= oach to Filtering Junk E-mail.. AAAI'98 Workshop on Learning for Text Categorization, July 27, 1998, Madison, Wisconsin.

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H. Drucker, Donghui Wu, and V.N. Vapnik. Support vector machine for= spam categorization. IEEE Transactions on Neural Networks, 10(5):1048–1054, September 1999.

Robert Hall. A countermeasure to duplicate-detecting anti-spam techniques.,Technical Report 99.9.1, AT&T Research Labs, 1999.

H. Katirai. Filtering junk e-mail: A performance comparison between genetic programming naive bayes. Available: ht= tp://members.rogers.com/hoomank/papers/katirai99filtering.pdf , 1999.

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I. Androutsopoulos, J. Koutsias<= /span>, K. Chandrinos, G. Palioura= s, and C. Spyropoulos. An eval= uation of naive bayesian anti-spam filtering. I= n G. Potamias,V. Moustakis, and M. van Someren, editors, Proceedings of the Workshop on Machine Learning in the New Informat= ion Age, pages 9-17, 2000.

Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Sakkis G= ., Spyropoulos, C. and P. Stamatopo= ulos, Learning to filter spam-email: A comparision of= a naïve Bayesian and memory-based approach. In workshop on Machine Learning and Textual Informaion Access, 4^t=
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Yanlei Diao= , Hongjun Lu, and Dekai Wu.= A comparative study of classific= ation-based personal e-mail filtering. In Takao Terano, Huan Liu, and Arbee L. P.= Chen, editors, Proceedings of PAKDD-00, 4= th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 408–419, Kyoto, JP, 2000.

I. Androutsopoulos, J. Koutsias<= /span>, K. V. Chandrinos, and C. D. Spyropoulos. An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages.= In Proceedings of SIGIR-2000, pages 1= 60-167. ACM, 2000.

G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C. Spyropoulos, and P. Sta= matopoulos. A memory-based approach to anti-spam filtering. TechReport DEMO 2001, National Centre for Scientific Research “Demokritos”, 2001.

Georgios Sakkis, Ion Androutsopou= los, Georgios Paliouras, Vange= lis Karkaletsis, Constantine D. Spyr= opoulos, Panagiotis Stamatopoulos. Stacking Class= ifiers for Anti-Spam Filtering of E-mail.&= nbsp; In Proceedings of the 6th Conference on Empirical Methods in Natural Language Processing (EMNLP 2001)= , pp. 44–50, Carnegie Mellon University, Pittsburgh, PA, USA, 2001.

A. Kolcz and J. Alspector. SVM-based filtering of email spam with con= tent-specific misclassification costs. In Proceedings of the TextDM'01 Worksh= op on Text Mining - held at the 2001 IEEE International Conference on Data Mining, 2001.=

X. Carreras and L. Marquez. Boosting trees for antispam email filtering. In Proceedings of RANLP-2001, 4th International Conference on Recent Advances in Natural Language Processing,= 2001.

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J. M. G&oacu= te;mez Hidalgo. Evaluating cost-sensitive unsoli= cited bulk email categorization. In Proceedings of SAC-02, 17th ACM Symposium on Applied Computing, pages 615-620, Madrid, ES, 2002.

A. Back, "HashCash -- A Denial of Service Counter-Measure"= (5 years on), Tech Report, 2002.

S. E. Fahlman. Selling interrupt rights: A way to control unwanted email and telephone calls. IBM Systems Journal, 41(4):759-766, 2002

S.Hird. Technical Solutions for Controlling Spam In the proceedings of AUUG2002, Melbourne, 4-6 Sep= tember, 2002

Hidalgo, J., (2002) Evaluating cost-sensitive Unsolicited Bu= lk Email categorization, Proceedins of t= he 2002 ACM symposium on Applied Computing http://portal.acm.org/citation.cfm?id=3D508911&dl=3DACM&am= p;coll=3Dportal

K. Schneider. A comparison of event models for naïve bayes anti-spam e-mail filtering. In Proceedings of the 1= 1th Conference of the European Chapter of the Association for Computational Linguistics (EACL'03), 2003.

K. Schneider. Fighting spam in re= al time. In Proceedings of the 2003 Spam Conference, Jan 2003. Available: http://www.brightmail.com/press/2003_MIT_Spam_Conference/ .

L. Weinstein. Inside risks: Spam wars. CACM, 46(8):136, Aug 2003.

C. Dwork, A. Goldberg, and M. Na= or, "On Memory-Bound Functions for Fighting Spam", Proceedings of the 2= 3rd Annual International Cryptology Conference (CRYPTO 2003), August 2003. =

John Ioannidis , Fighting Spa= m by Encapsulating Policy in Email Addresses,
Proceedings of Network and Distribu= ted Systems Security Conference (NDSS), 2003.

M. Abadi, M. Burrows, M. Manasse, and T. Wobber. "Moderately Hard, Memory-bound Functions", Proceedings of the 10th Annual Network and Distributed S= ystem Security Symposium, February 2003.

Center for Democracy and Technolo= gy. Why am I getting all this spam? Unsolicited commerci= al email six month report. Available: http://www.c= dt.org/speech/spam/030319spamreport.shtml, March 2003.

P. Cunningham, N. Nowlan, S. J. Delany, and M. Haahr= . A case-based approach to spam filtering that can track concept drift. In The ICCBR'03 Works= hop on Long-Lived CBR Systems, Trondheim, Norway, June 2003. Available from: http://www.cs.tcd.ie/publications/tech-reports/reports.03/TCD-CS= -2003-16.pdf

K. Eide. Winning the war on spam: Comparison of bayesian spam fi= lters. Available: = http://home.dataparty.no/kristian/reviews/bayesian/ , August 2003.

P. Graham. Better bayesian filtering. Available: http://www.pau= lgraham.com/better.html , Jan 2003.

R. Kraut, S. Sunder, J. Morris, M. Cronin, and D. Fil= er. Markets for attention: Will postage for email help? In ACM Conference on CSCW, pages 206-215, 2003.<= /p>

Tom Fawcett, = “ ‘In vivo’ spam filtering: A Challe= nge Problem for Data Mining”, KDD Explorations vol.5 no= .2, December 2003

J. Graham-Cumming. Field Guide to= Spam. Sophos, 2003. Available from http://www.activestate.com/Products/PureMessage/Field_Guide_to_Spam/ and updated periodically.

Kevin R. Gee. Using latent semant= ic indexing to filter spam, 2003. ACM Symposium on Applied Computing, Data Mining Track, 2003.

Trevor Stone, Parame= terization of Naïve Bayes for Spam Filters, Masters Comphrehe= nsive Exam, University of Colorado at Boulder,2003.

T. Tompkins and D. Handley. Giving e-mail back to the users: Using digital signatures to solve the spam problem. First Monday, 8(9), September 2003. http://= firstmonday.org/issues/issue8_9/tompkins/index.html <= /span>

David Madigan, Statistics a= nd the War on Spam in Statist= ics, A Guide to the Unknown, 2003.

M. Abadi, A. Birrell, M. Burrows, F. Dabek, and T. Wobber. "Bank= able Postage for Network Services", Proceedings of the 8th Asian Computing Science Conference, Mumbai, India, December 2003..

Thede Loder, Marshall Van Alstyne= , and Rick Wash. An Economic So= lution to the Spam Problem. ACM E-Commerce, 2004

Hulten, Geoff, Goodman, Joshua and Rounthwaite, Robert. Filtering Spam= E-mail on a Global Scale. World Wide Web Conference, 2004.

Goodman, Joshua and Rounthwaite, = Robert. Stoppi= ng Outgoing Spam, ACM Conference on E-Commerce, May 2004.

Goodman, Joshua and Heckerman, Da= vid. Stopping Spam with Statistics. Significance Magazine, to appear.

Goodman, Joshua and Rounthwaite, = Robert. SmartProof. To appear.

Goodman, Joshua.&nbs= p; IP Addresses and Clients. To appear.<= /o:p>

T. V. Zandt. Information overload in a network of tar= geted communication. RAND (forthcoming).

Don’t forget to look at the proceedings for CEAS-2004, the First Conference on Email and AntiSpam, http://www.= ceas.cc