Spam classification using adaptive boosting algorithm

Proceedings - 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007; 1st IEEE/ACIS International Workshop on e-Activity, IWEA 2007

Volumn , Issue , 2007, Pages 972-976

  Shawkat Ali, A B M ^a   Yang, Xiang ^a  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

Author keywords

Boosting algorithm; Filtering; Spam

Indexed keywords

ADAPTIVE ALGORITHMS; ALGORITHMS; BOOLEAN FUNCTIONS; CLASSIFICATION (OF INFORMATION); COMMUNICATION; CYBERNETICS; FILTRATION; INFORMATION MANAGEMENT; INFORMATION SCIENCE; KNOWLEDGE MANAGEMENT; SPAMMING; TURBULENT FLOW; WAVE FILTERS;

ADAPTIVE BOOSTING; COMPUTATION TIME; END USERS; FALSE POSITIVE RATE (FPR); INTERNATIONAL (CO); INTERNATIONAL CONFERENCES; SPAM CLASSIFICATION; SPAM FILTERING; TRUE POSITIVE (TP) RATE;

INTERNET;

EID: 46749089265     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIS.2007.170     Document Type: Conference Paper

References (27)

1. Aladdin Knowledge Systems, "Anti-Spam White Paper", http://www.esafe.com/pdf/esafe/esafe_antispam_whitepaper.pdf, 2004.
2. A. Grove and D. Schuurmans, "Boosting in the limit: Maximizing the margin of learned ensembles," in Proceedings of the Fifteenth National Conference on Artifical Intelligence, pp. 692-699, 1998.
3. Australian Spam Act 2003, http://www.noie.gov.au/publications/NOIE/spam/ Act2003/, 2003.
4. Blackspider Technologies Ltd., "MailControl Spam Technology Overview", http://www.blackspider.com/services-spam_datasheet.pdf, 2003.
5. Brightmail Inc. http://www.brightmail.com, 2006.
6. Clearswift Corporation, http://www.clearswift.com/, 2006.
7. Cloudmark company, http://www.cloudmark.com/, 2006.
8. Commission of The European Communities, "Communication form the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions on unsolicited commercial communications or 'spam'", COM(2004) 28 final, 2004.
9. Eric Allman, "The FTC and Spam", ACM Queue, Vol. 1, No. 6, 2003.
10. Ferris Research report: "Spam Control: Problems and Opportunities," 2003, http://www.ferris.com/rep/200301/report.pdf.
11. Gary Robinson, "A Statistical Approach to the Spam Problem", Linux Journal, Vol. 2003, No. 107, 2003.
12. G.H. John and P. Langley, "Estimating continuous distributions in Bayesian classifiers," in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, San Mateo, CA, pp. 338-345, 1995.
13. Hakank Spam Data, http://www.hakank.org/data_mining_presentation/data/ index.html, 2007.
14. I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2005.
15. Network Associates Inc., http://www.nai.com/, 2006.
16. Nucleus Research report: "Spam: The Silent ROI Killer," July, 2003, http://www.nucleusresearch.com/research/d59.pdf.
17. Paris Trudeau, Richard Cullen and Dave Zwieback, "Major Techniques for Classifying Spam", April 2003.
18. R. E. Schapire, "The strength of weak learnability," Machine Learning, vol. 5, no. 2 pp 197-227, 1990.
19. R. Jin, Y. Liu, L. Si, J. Carbonell and A. Hauptmann, "A New Boosting Algorithm Using Input-Dependent Regularizer," in Proceedings of Twentieth International Conference on Machine Learning (ICML'03) 2003.
20. SurfControl Company, http://www.surfcontrol.com/, 2006.
21. Symantec Corporation, http://www.symantec.com/, 2006.
22. Trend Micro Inc., http://www.trendmicro.com/, 2006.
23. Tumbleweed Communications Corp. http://www.tumbleweed.com/, 2006.
24. Y. Freund and R. E. Schapire, "A decision theoretic generalization of online learning and an application to boosting", Unpublished manuscript available electronically (by email request). An extended abstract appeared in Computational Learning Theory: Second European Conference, EuroCOLT '95, pp 23-37, Springer-Verlag, 1995.
25. Y. Freund, "Boosting a weak learning algorithm by majority," Information and Computation, vol.121 no. 2 pp 256-285, 1995.
26. Y. Freund and R.E. Schapire, "Experiments with a new boosting algorithm," In the Proceeding of International Conference on Machine Learning, pp 148-156, 1996.
27. Zix Corporation, http://www.zixcorp.com, 2006.