Symbiotic filtering for spam email detection

Expert Systems with Applications

Volumn 38, Issue 8, 2011, Pages 9365-9372

  Lopes, Clotilde ^a   Cortez, Paulo ^a   Sousa, Pedro ^a   Rocha, Miguel ^a   Rio, Miguel ^b  

^a UNIVERSITY OF MINHO   (Portugal)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Anti spam filtering; Collaborative filtering; Content Based Filtering; Naive Bayes; Word attacks

Indexed keywords

ANTI-SPAM FILTERING; COLLABORATIVE FILTERING; CONTENT-BASED FILTERING; NAIVE BAYES; WORD ATTACKS;

SIGNAL FILTERING AND PREDICTION;

INTERNET;

EID: 79953711706     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.01.174     Document Type: Article

References (30)

1. E. Alocilja Principles of biosystems engineering 2000 Erudition Books Massachusetts, USA
2. R. Beckermann, A. McCallum, and G. Huang Automatic categorization of email into folders: benchmark experiments on Enron and SRI corpora. Ir-418 2004 University of Massachusetts Amherst
3. Chang, M.; Yih, W.; & Meek, C. (2008). Partitioned logistic regression for spam filtering. In 14th ACM SIGKDD int. conference on knowledge discovery and data mining (pp. 97-105).
4. Cheng, V. & Li, C. (2006). Personalized spam filtering with semi-supervised classifier ensemble. In IEEE/WIC/ACM international conference on Web intelligence.
5. T. Fawcett An introduction to ROC analysis Pattern recognition letters 27 8 2006 861 874
6. F. Fdez-Riverola, E.L. Iglesias, F. Díaz, J.R. Méndez, and J.M. Corchado Applying lazy learning algorithms to tackle concept drift in spam filtering Expert Systems with Applications 33 1 2007 36 48 (Pubitemid 44959915)
7. I. Feinerer, K. Hornik, and D. Meyer Text Mining Infrastructure in R Journal of Statistical Software 25 5 2008 1 54
8. Flexer, A. (1996). Statistical evaluation of neural networks experiments: Minimum requirements and current practice. In Proceedings of the 13th European meeting on cybernetics and systems research, Vienna, Austria (Vol. 2, pp. 1005-1008).
9. Garg, A.; Battiti, R.; & Cascella, R. (2006). May I borrow your filter? Exchanging filters to combat spam in a community. In Advanced information networking and applications, 2006. AINA 2006. 20th International conference (Vol. 2).
10. S. Garriss, M. Kaminsky, M. Freedman, B. Karp, D. Maziéres, and H. Yu RE: Reliable email Proceedings of the 3rd conference on Networked Systems Design and Implementation (NSDI) 2006 USENIX Association Berkeley CA San Jose, CA, USA 297 310
11. Gray, A.; & Haahr, M. (2004). Personalised, collaborative spam filtering. In 1st Conference on e-mail and anti-spam CEAS.
12. R. Grossman, M. Hornick, and G. Meyer Data mining standards initiatives Communications of ACM 45 8 2002 59 61 (Pubitemid 135701842)
13. T. Guzella, and W. Caminhas A review of machine learning approaches to spam filtering Expert Systems with Applications 36 2009 10206 10222
14. Hershkop, S.; & Stolfo, S. (2005). Combining email models for false positive reduction. In 11th ACM SIGKDD int. conference on knowledge discovery and data mining (pp. 21-24).
15. C. Kanich, C. Kreibich, K. Levchenko, B. Enright, G. Voelker, and V. Paxson Spamalytics: An empirical analysis of spam marketing conversion Computer and Communications Security Conference (CCS'08) 2008 ACM 27 31
16. R. Kewley, M. Embrechts, and C. Breneman Data strip mining for the virtual design of pharmaceuticals with neural networks IEEE Transactions on Neural Networks 11 3 2000 668 679
17. J. Kong, B. Rezaei, N. Sarshar, V. Roychowdhury, and P. Boykin Collaborative spam filtering using e-mail networks IEEE Computer 39 8 2005 67 73
18. Kosmopoulos, A.; Paliouras, G.; & Androutsopoulos, I. (2008). Adaptive spam filtering using only Naive Bayes text classifiers. In CEAS 2008 - Fifth conference on email and anti-spam.
19. G. Lai, C. Chen, C. Laih, and T. Chen A collaborative anti-spam system Expert Systems with Applications 36 2009 6645 6653
20. T. Loder, M. Van Alstyne, and R. Wash An economic answer to unsolicited communication Proceedings of the 5th ACM conference on electronic commerce 2004 ACM New York, NY, USA 40 50
21. MAAWG (2009). Email metrics program: The network operators' perspective. Report #10 - Third and fourth quarter 2008. Messaging Anti-Abuse Working Group, S. Francisco CA, USA.
22. J. Méndez, I. Cid, D. Glez-Peña, M. Rocha, and F. Fdez-Riverola A comparative impact study of attribute selection techniques on Naïve Bayes spam filters 8th industrial conference on data mining Vol. LNAI 5077 2008 Springer 213 227
23. Metsis, V.; Androutsopoulos, I.; & Paliouras, G. (2006). Spam filtering with Naive Bayes - Which Naive Bayes? In Third conference on email and anti-spam (CEAS) (pp. 125-134).
24. B. Nelson, M. Barreno, F. Chi, A. Joseph, B. Rubinstein, and U. Saini Exploiting machine learning to subvert your spam filter 1st usenix workshop on large-scale exploits and emergent threats 2008 ACM Press 1 9
25. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN:3-900051-00-3, http://www.R-project.org.
26. Ramachandran, A. & Feamster, N. (2006). Understanding the network-level behavior of spammers. In ACM (Ed.), SIGCOMM'06 (pp. 291-302). (Pubitemid 44623735)
27. M. Walfish, J. Zamfirescu, H. Balakrishnan, D. Karger, and S. Shenker Distributed quota enforcement for spam control Proceedings of the 3rd conf. on networked systems design and implementation (NSDI) 2006 USENIX Association Berkeley San Jose, CA 281 296
28. Wong, M. (2005). Sender authentication: What to do. White paper.
29. K. Yu, A. Schwaighofer, V. Tresp, W. Ma, and H. Zhang Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical Bayes 19th Int. conf. on uncertainty in artificial intelligence (UAI) 2003 AMC 353 360
30. Zhong, Z.; Ramaswamy, L.; & Li, K. (2008). ALPACAS: A large-scale privacy-aware collaborative anti-spam system. In IEEE INFOCOM (pp. 556-564).