Article

Filtering spam e-mail on a global scale

Authors:

G. Hulten,

J. Goodman,

R. RounthwaiteAuthors Info & Claims

WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters

Pages 366 - 367

https://doi.org/10.1145/1013367.1013478

Published: 19 May 2004 Publication History

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Abstract

In this paper we analyze a very large junk e-mail corpus which was generated by a hundred thousand volunteer users of the Hotmail e-mail service. We describe how the corpus is being collected, and analyze: the geographic origins of the e-mail who the e-mail is targeting and what the e-mail is selling.

Reference

[1]

FTC Division of Marketing Practice. False claims in spam. http://www.ftc.gov/opa/2003/04/spamrpt.htm. April 30, 2003

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Neeharika CKalaiarasi S(2023)Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracyE3S Web of Conferences10.1051/e3sconf/202339904025399(04025)Online publication date: 12-Jul-2023
https://doi.org/10.1051/e3sconf/202339904025
Rustam FSaher NMehmood ALee EWashington SAshraf I(2023)Detecting ham and spam emails using feature union and supervised machine learning modelsMultimedia Tools and Applications10.1007/s11042-023-14814-282:17(26545-26561)Online publication date: 8-Mar-2023
https://doi.org/10.1007/s11042-023-14814-2
Zitar RHamdan A(2013)Genetic optimized artificial immune system in spam detection: a review and a modelArtificial Intelligence Review10.1007/s10462-011-9285-z40:3(305-377)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1007/s10462-011-9285-z
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Index Terms

Filtering spam e-mail on a global scale
1. Security and privacy
  1. Human and societal aspects of security and privacy
2. Social and professional topics
  1. Computing / technology policy

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Information & Contributors

Information

Published In

WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters

May 2004

532 pages

ISBN:1581139128

DOI:10.1145/1013367

Conference Chairs:
Stuart Feldman
IBM Research
,
Mike Uretsky
New York University
,
Program Chairs:
Marc Najork
Microsoft Research
,
Craig Wills
Worcester Polytechnic Institute

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 May 2004

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WWW04

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WWW04: The 2004 World Wide Web Conference ( in conjunction with ACM Conference on Electronic Commerce [EC'04])

May 19 - 21, 2004

NY, New York, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all

Neeharika CKalaiarasi S(2023)Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracyE3S Web of Conferences10.1051/e3sconf/202339904025399(04025)Online publication date: 12-Jul-2023
https://doi.org/10.1051/e3sconf/202339904025
Rustam FSaher NMehmood ALee EWashington SAshraf I(2023)Detecting ham and spam emails using feature union and supervised machine learning modelsMultimedia Tools and Applications10.1007/s11042-023-14814-282:17(26545-26561)Online publication date: 8-Mar-2023
https://doi.org/10.1007/s11042-023-14814-2
Zitar RHamdan A(2013)Genetic optimized artificial immune system in spam detection: a review and a modelArtificial Intelligence Review10.1007/s10462-011-9285-z40:3(305-377)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1007/s10462-011-9285-z
Mohammad AZitar R(2011)Application of genetic optimized artificial immune system and neural networks in spam detectionApplied Soft Computing10.1016/j.asoc.2011.02.02111:4(3827-3845)Online publication date: 1-Jun-2011
https://dl.acm.org/doi/10.1016/j.asoc.2011.02.021
Raghunandan H(2009)Autonomic E-mail Services for Better Storage ManagementProceedings of the 2009 World Conference on Services - II10.1109/SERVICES-2.2009.13(25-32)Online publication date: 21-Sep-2009
https://dl.acm.org/doi/10.1109/SERVICES-2.2009.13
Famil saeedian MBeigy H(2009)Dynamic classifier selection using clustering for spam detection2009 IEEE Symposium on Computational Intelligence and Data Mining10.1109/CIDM.2009.4938633(84-88)Online publication date: Mar-2009
https://doi.org/10.1109/CIDM.2009.4938633
Kim JChung KChoi K(2007)Spam Filtering With Dynamically Updated URL StatisticsIEEE Security and Privacy10.1109/MSP.2007.955:4(33-39)Online publication date: 1-Jul-2007
https://dl.acm.org/doi/10.1109/MSP.2007.95
Zhao SYu FZhao B(2007)An Energy Efficient Directed Diffusion Routing ProtocolProceedings of the 2007 International Conference on Computational Intelligence and Security10.1109/CIS.2007.94(1067-1072)Online publication date: 15-Dec-2007
https://dl.acm.org/doi/10.1109/CIS.2007.94
Cook DHartnett JManderson KScanlan J(2006)Catching spam before it arrivesProceedings of the 2006 Australasian workshops on Grid computing and e-research - Volume 5410.5555/1151828.1151851(193-202)Online publication date: 1-Jan-2006
https://dl.acm.org/doi/10.5555/1151828.1151851
Melville NStevens APlice RPavlov O(2006)Unsolicited Commercial E-MailInternational Journal of Electronic Commerce10.2753/JEC1086-441510040510:4(143-170)Online publication date: 1-Jun-2006
https://dl.acm.org/doi/10.2753/JEC1086-4415100405

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