SpamCloud: A mapreduce based anti-spam architecture

Proceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

Volumn 6, Issue , 2010, Pages 3003-3006

  Caruana, Godwin ^a   Li, Maozhen ^a   Qi, Hao ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Algorithms; Architectures; Distributed computing; Performance; Text processing

Indexed keywords

ANTI-SPAM; DISTRIBUTED COMPUTING; MAP-REDUCE; PERFORMANCE; SPAM FILTERING;

ALGORITHMS; FUZZY SETS; TEXT PROCESSING; WORD PROCESSING;

INTERNET;

EID: 78649239474     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FSKD.2010.5569282     Document Type: Conference Paper

References (27)

1. MessageLabs (2009) MessageLab Intelligence. Symantec. [Online] Available at http://www.messagelabs.com/mlireport/MLIReport-2009-05-May-FINAL.pdf (Accessed on 20/06/2009)
2. Blanzieri, E and Bryl, A. A survey of learning-based techniques of email spam filtering. Artif. Intell. Rev. 29, 1 (Mar. 2008), 63-92. DOI=http://dx.doi.org/10.1007/s10462-009-9109-6
3. Levine, J.R. Experiences with greylisting. In Second Conference on Email and Anti-Spam - CEAS (2005).
4. Hunt, R. and Carpinter, J. Tightening the net: A review of current and next generation spam filtering tools Computers & Security, vol. 25, (2006), pp. 566-78.
5. Schryen, G. "Armed for the Spam Battle: A Technological and Organizational Infrastructure Framework." In HICSS, 144, (2007)
6. Conry-Murray, A. The antispam cocktail: mix it up to stop junk E-mail Network Magazine, vol. 19, (2004), pp. 33-8.
7. Wu, C. "Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks," Expert Syst. Appl., vol. 36, (2009), pp. 4321-4330.
8. Ramachandran, A; Feamster, N and Vempala, S.Filtering spam with beahavioural blacklisting. In Proceedings of the 14th ACM Conference on Computer and Communucations Security (Alexandria, Virginia, USA, October 28-31, 2007). CCS '07, New York, NY, 342-351. DOI=http://doi.acm.org/10.1145/1315245.1315288 (2007)
9. Dean, Jeffrey and Ghemawat Sanjay (2004). Simplifed Data Processing on Large Clusters. Google Inc. [Online] Available at http://labs.google.com/papers/ mapreduce-osdi04.pdf (Accessed on 18/06/2009)
10. Aarnio, T. "Parallel Data Processing with Mapreduce". Helsinki University Of Technology. [Online] Available online at http://www.cse.tkk.fi/en/ publications/b/5/papers/aarnio-final.pdf (2009) (Accessed on 02/01/2010)
11. Apache Hadoop. Hadoop. [Online] Available at http://hadoop.apache.org/ (Accessed on 01/01/2010)
12. Caruana, G and Darbyshire, P (Unpublished). A Distributed and Interoperable Anti-Spam Architecture Using Plug-ins. [Online] Available at http://cv.s0nic.net/spampaper.pdf (Accessed on 18/06/2009)
13. Colas, F. and Brazdil, F. Comparison of SVM and Some Older Classification Algorithm in Text Classification Tasks. IFIP International Federation for Information Processing. Volume 217/2006. 978-0-387-34654-0. 169-178 (2006)
14. Cristianini Nello and Shawe-Taylor J. An Introduction to SVM's and Other Kernel-based Learning Methods. Cambridge University Press. ISBN:0521780195.
15. Do, T., Nguyen, V., and Poulet, F. Speed Up SVM Algorithm for Massive Classification Tasks. In Proceedings of the 4th international Conference on Advanced Data Mining and Applications (Chengdu, China, October 08 - 10, 2008). C. Tang, C. X. Ling, X. Zhou, N. J. Cercone, and X. Li, Eds. Lecture Notes In Artificial Intelligence, vol. 5139. Springer-Verlag, Berlin, Heidelberg, 147-157. (2008) DOI=http://dx.doi.org/10.1007/978-3-540-88192-6-15
16. Li, K Li, K, Huang, H and Ti, S. Active learning with simplified SVM's for Spam categorization. Proceedings of the International Conference on Machine Learning and Cybernetics (2002) Volume: 3, On page(s): 1198- 1202 vol.3.
17. Platt, J. C. Fast training of SVM's using sequential minimal optimization. In Advances in Kernel Methods: Support Vector Learning, B. Schölkopf, C. J. Burges, and A. J. Smola, Eds. MIT Press, Cambridge, MA, (1999) 185-208.
18. Scholkopf, B. And Smola, A.J. Learning with Kernels: SVM's, Regularization, Optimization, AND Beyond, Cambridge, MA, USA: MIT Press, (2001).
19. Syed, N.a. Huan, S. Kah, L. and Sung, K. Incremental Learning with SVM's, (1999).
20. Herschkop, S. and Stolfo, S. J. Combining email models for false positive reduction. In Proceedings of the Eleventh ACM SIGKDD international Conference on Knowledge Discovery in Data Mining (Chicago, Illinois, USA, August 21 - 24, 2005). KDD '05. ACM, New York, NY, 98-107. (2005) DOI=http://doi.acm.org/10. 1145/1081870.1081885
21. Drucker, H.; Member, S.; Wu, D Member, S. and Vapnik, V.N. "Support vector machines for spam categorization," IEEE Transactions on Neural Networks, vol. 10, 1999, pp. 1048-1054.
22. Suykens, J. A. K., Gestel, T. V. Brabanter, J. D., Moor, B. D. and Vandwalle, J.. Least Squares Support Vector Machines. World Scientific Pub. Co., Singapore, 2002.
23. Zuo, H., Hu, W., Wu, O., Chen, Y., and Luo G. Detecting image Spam using local invariant features and pyramid match kernel. In Proceedings of the 18th international Conference on World Wide Web (Madrid, Spain, April 20 - 24, 2009). WWW '09. (2009) ACM, New York, NY, 1187-1188. DOI= http://doi.acm.org/10.1145/ 1526709.1526921
24. Bach F.R. and Jordan, M.I. "Predictive low-rank decomposition for kernel methods," ICML, (2005), pp. 33-40.
25. Zanghirati, G. and Zanni, L "A parallel solver for large quadratic programs in training support vector machines," Parallel Comput., vol. 29, 2003, pp. 535-551.
26. Catanzaro, B., Sundaram, N., and Keutzer, K.. Fast SVM training and classification on graphics processors. In Proceedings of the 25th international Conference on Machine Learning (Helsinki, Finland, July 05 - 09, 2008). ICML '08, vol. 307. ACM, New York, NY, 104-111. (2008) DOI= http://doi.acm.org/10. 1145/1390156.1390170
27. Web Ontology Language (2004) [Online] Available at http://www.w3.org/TR/ owl-features/. (Accessed on 18/06/2009)