Email spam detection: A symbiotic feature selection approach fostered by evolutionary computation

International Journal of Information Technology and Decision Making

Volumn 12, Issue 4, 2013, Pages 863-884

  Sousa, Pedro ^a   Cortez, Paulo ^a   Vaz, Rui ^a   Rocha, Miguel ^a   Rio, Miguel ^b  

^a UNIVERSITY OF MINHO   (Portugal)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

content based filtering; evolutionary algorithms; feature selection; Na ve Bayes; Spam detection

Indexed keywords

EID: 84882738506     PISSN: 02196220     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219622013500326     Document Type: Article

References (29)

1. MAAWG, Email metrics program: The network operators perspective, Report # 15-First, second and third quarter 2011, Technical Report, Messaging Anti-Abuse Working Group (2011
2. G. Schryen, Anti-Spam Measures: Analysis and Design (Springer, 2007
3. OECD, OECD guidelines for the security of information systems and networks: Towards a culture of security, Technical report (2006
4. A. Ramachandran and N. Feamster, Understanding the network-level behavior of spammers, ACM SIGCOMM 2006 Conf. (2006), pp. 291-302
5. J. Mendez, I. Cid, D. Glez-Peña, M. Rocha and F. Fdez-Riverola, A comparative impact study of attribute selection techniques on naive bayes spam filters, 8th Industrial Conf. Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (2008), pp. 213-227
6. E. Blanzieri and A. Bryl, A survey of learning-based techniques of email spam filtering, Artificial Intelligence Review 29(1) (2008) 63-92
7. Y. Peng, K. Gang, S. Young and Z. Chen, A descriptive framework for the field of data mining and knowledge discovery, International Journal of Information Technology & Decision Making 7(4) (2008) 639-682
8. S. Ho, S. Lui and W. Ma, Acceptance of internet content filters: An empirical study, International Journal of Information Technology & Decision Making 2(3) (2003) 477-496
9. Q. Yang and X. Wu, 10 challenging problems in data mining research, International Journal of Information Technology & Decision Making 5(4) (2006) 597-604
10. A. Abdelwahab, H. Amira, I. Matsuba, Y. Horiuchi and S. Kuroiwa, Alleviating the sparsity problem of collaborative filtering using an e±cient iterative clustered prediction technique, International Journal of Information Technology & Decision Making 11(1) (2012) 33-53
11. A. Gray and M. Haahr, Personalised, collaborative spam filtering, 1st Conf. E-Mail and Anti-Spam (CEAS) (Mountain View, CA, USA, 2004
12. S. Garriss, M. Kaminsky, M. Freedman, B. Karp, D. Mazieres and H. Yu, RE: Reliable email, 3rd Conference on Networked Systems Design and Implementation (NSDI) (2006), pp. 297-310
13. C. Lopes, P. Cortez, P. Sousa, M. Rocha and M. Rio, Symbiotic filtering for spam email detection, Expert Systems with Applications 38(8) (2011) 9365-9372
14. K. De Jong, Evolutionary Computation: A Unified Approach (MIT Press, 2006
15. A. Lopez-Herrera, E. Herrera-Viedma and F. Herrera, A multiobjective evolutionary algorithm for spam e-mail filtering, 3rd Int. Conf. Intelligent System and Knowledge Engineering (2008), pp. 366-371
16. J. Dudley, L. Barone and L. While, Multi-objective spam filtering using an evolutionary algorithm, IEEE Congress on Evolutionary Computation (2008), pp. 123-130
17. Y. Zhang, H. Li, M. Niranjan and P. Rockett, Applying cost-sensitive multiobjective genetic programming to feature extraction for spam e-mail filtering, 11th European Conf. Genetic Programming (2008), pp. 325-336
18. V. Metsis, I. Androutsopoulos and G. Paliouras, Spam filtering with naive bayes which naive bayes? 3rd Conf. Email and AntiSpam (CEAS) (2006), pp. 125-134
19. G. Cormack and T. Lynam, TREC 2005 spam track overview, The Fourteenth Text REtrieval Conf. (TREC 2005) Proceedings (2005), pp. 1-17
20. T. Fawcett, In vivo spam filtering: A challenge problem for KDD, SIGKDD Explorations 5(2) (2003) 140-148
21. T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters 27(8) (2006) 861-874
22. A. Flexer, Statistical evaluation of neural networks experiments: Minimum requirements and current practice, 13th European Meeting on Cybernetics and Systems Research (1996), pp. 1005-1008
23. I. Mierswa, M. Wurst, R. Klinkenberg, M. Scholz and T. Euler, Yale: Rapid prototyping for complex data mining tasks, 12th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (2006), pp. 935-940
24. I. Guyon and A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research 3 (2003) 1157-1182
25. P. Evangelista, P. Maia and M. Rocha, Implementing metaheuristic optimization algorithms with JECoLi, Ninth Int. Conf. Intelligent Systems Design and Applications (2009), pp. 505-510
26. N. Radcliffe, Genetic set recombination, Foundations of Genetic Algorithms 2 (1993) 203-219
27. A. Schwartz, SpamAssassin (O'Reilly, 2005
28. Z. Palotai, B. Gabor and A. Lörincz, Adaptive highlighting of links to assist surfing on the internet, International Journal of Information Technology & Decision Making 4(1) (2005) 117-139
29. D. Canali, M. Cova, G. Vigna and C. Kruegel, Prophiler: A fast filter for the large-scale detection of malicious web pages, 20th Int. Conf. World Wide Web (2011), pp. 197-206