A study of spam filtering using support vector machines

Artificial Intelligence Review

Volumn 34, Issue 1, 2010, Pages 73-108

  Amayri, Ola ^a   Bouguila, Nizar ^b  

^a Gina Cody School of Engineering and Computer Science   (Canada)

^b NONE   (Canada)

Author keywords

Feature mapping; Online active; Spam filtering; String kernels; Support vector machines

Indexed keywords

ACTIVE METHOD; CONTINUOUS DATA; DATA PREPROCESSING; DISTANCE-BASED; ELECTRONIC COMMUNICATIONS; EMPIRICAL RESULTS; FEATURE MAPPING; FEATURE SPACE; FEATURE VECTORS; MACHINE-LEARNING; ONLINE METHODS; PRECISION AND RECALL; REAL TIME; SPAM EMAILS; SPAM FILTERING; STRING KERNEL; TEXT CLASSIFICATION;

COMMUNICATION SYSTEMS; ELECTRONIC MAIL; INTERNET; MAPPING; TEXT PROCESSING; THERMAL CONDUCTIVITY; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 77953609277     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10462-010-9166-x     Document Type: Article

References (65)

1. Abadi M, Burrows M, Manasse M, Wobber T (2003) Moderately hard, memory-bound functions. In: Proceedings of the 10th annual network and distributed system security symposium, California, USA, pp 25-39
2. T Anderson R Bahadur 1962 Classification into two multivariate normal distributions with different covariance matrices Ann Math Stat 33 2 420 431 0113.13702 10.1214/aoms/1177704568 141198
3. Androutsopoulos I, Koutsias J, Chandrinos K, Paliouras G, Spyropoulos C (2000a) An evaluation of naive bayesian anti-spam filtering. In: Proceedings of the 11th European conference on machine learning, Barcelona, Spain, pp 9-17
4. Androutsopoulos I, Paliouras G, Karkaletsis V, Sakkis G, Spyropoulos C, Stamatopoulos P (2000b) Learning to filter spam e-mail: A comparison of a naive bayesian and a memory-based approach. In: Proceedings of the workshop on machine learning and textual information access, 4th european conference on principles and practice of knowledge discovery in databases, Lyon, France, pp 1-13
5. Back A (2002) Hashcash-a denial of service counter-measure. http://cypherspace.org/hashcash/hashcash/.pdf
6. Berg Ch, Christensen JPR, Bessel P (1984) Harmonic analysis on semigroups. Theory of positive definite and related functions. Graduate texts in mathematics, vol 100. Springer-Verlag, New York
7. E Blanzieri A Bryl 2006 A survey of learning-based techniques of email spam filtering Artif Intell Rev 29 1 63 92 10.1007/s10462-009-9109-6
8. Brinker K (2003) Incorporating diversity in active learning with support vector machines. In: Proceedings of the twentieth international conference on machine learning, pp 59-66
9. Caropreso MF, Matwin S, Sebastiani F (2001) Text databases and document management: theory and practice, IGI Publishing, chap A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization, pp 78-102
10. Carreras X, Marquez L (2001) Boosting trees for anti-spam email filtering. In: Proceedings of the 4th international conference on recent advances in natural language processing, Bulgaria, pp 58-64
11. Cauwenberghs G, Poggio T (2000) Incremental and decremental support vector machine learning. In: Proceedings of the neural information processing systems (NIPS), pp 409-415
12. Chang EY, Tong S, Goh K, Chang C (2001) Support vector machine concept-dependent active learning for image retrieval. In: Proceedings of the ACM international conference on multimedia, pp 107-118 (Pubitemid 32962460)
13. D Cohn L Atlas R Ladner 1994 Improving generalization with active learning Mach Learn 15 2 201 221
14. Cormack GV, Bratko A (2006) Batch and on-line spam filter comparison. In: Proceedings of the third conference on email and anti-spam, California, USA
15. Cormack GV, Lynam TR (2005) Trec 2005 spam track overview. In: Proceedings of the fourteenth text retrieval conference (TREC05), Gaithersburg, MD
16. C Cortes V Vapnik 1995 Support-vector networks Mach Learn 20 1 273 329 0831.68098
17. Courant R, Hilbert D (1953) Methods of mathematical physics, vol 1. Interscience Publishers Inc., New York
18. Cranor LF, LaMacchia BA (1998) Spam! Commun ACM 41(8): 74-83
19. Cukier W, Cody S, Nesselroth E (2006) Genres of spam: expectations and deceptions. In: Proceeding of the 39th annual Hawaii international conference on system sciences, vol 3. Hawaii, USA
20. Debole F, Sebastiani F (2003) Supervised term weighting for automated text categorization. In: Proceedings of the ACM symposium on Applied computing, Florida, USA, pp 784-788
21. Drake C, Oliver J, Koontz E (2004) Anatomy of a phishing email. In: Proceeding of first conference on email and anti-Spam (CEAS), California, USA
22. H Drucker V Vapnik D Wu 1999 Support vector machines for spam categorization IEEE Trans Neural Netw 10 5 1048 1054 10.1109/72.788645
23. Dwork C, Naor M (1993) Pricing via processing or combating junk mail. In: 12th annual international cryptology conference on advances in cryptology, Springer, no. 740 in LNCS, pp 139-147
24. Fawcett T (2004) Roc graphs: notes and practical considerations for researchers. Techanical report. HP Laboratories, Palo Alto, USA
25. Gates B, Myhrvold N, Rinearson P (1995) The road ahead. Viking Penguin, New York
26. Goodman J (2003) Spam: technologies and policies. http://www.research. microsoft.com/~joshuago/spamtech.pdf
27. Graham P (2002) A plan for spam. http://www.paulgraham.com/spam.html
28. Hulten G, Goodman J (2003) Tutorial on junk mail filtering. http://research.microsoft.com/~joshuago/tutorialOnJunkMailFilteringjune4.pdf
29. Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. In: Nédellec C, Rouveirol C (eds) Proceedings of of ECML-98, 10th European conference on machine learning, Springer, Chemnitz, DE, 1398, pp 137-142
30. Joachims T (1999a) Advances in kernel methods: support vector machines learning, MIT Press, Cambridge, MA, USA, chap Making large-scale support vector machine learning practical, pp 169-184
31. Joachims T (1999b) Transductive inference for text classification using support vector machines. In: Proceedings of the sixteenth international conference on machine learning (ICML-99), San Francisco, US, pp 200-209
32. N Kasabov S Pang 2004 Transductive support vector machines and applications in bioinformatics for promoter recognition Neural Inf Process 3 2 31 38
33. J Kivinen A Smola R Williamson 2004 Online learning with kernels IEEE Transac Signal Process 52 8 2165 2176 10.1109/TSP.2004.830991 2085578
34. Kolcz A, Alspector J (2001) Svm-based filtering of e-mail spam with content-specific misclassification costs. In: Proceedings of the Workshop on text mining, California, USA, pp 123-130
35. KW Lau QH Wu 2003 Online training of support vector machine Pattern Recognit 36 8 1913 1920 1054.68123 10.1016/S0031-3203(03)00038-4 (Pubitemid 36551678)
36. E Leopold J Kindermann 2002 Text categorization with support vector machines. How to represent texts in input space? Machine Learning 46 13 423 444 0998.68112 10.1023/A:1012491419635 (Pubitemid 34129978)
37. C Leslie R Kuang 2004 Fast string kernels using inexact matching for protein sequences J Mach Learn Res 5 1435 1455 2248023
38. Leslie C, Eskin E, Noble WS (2002) The spectrum kernel: a string kernel for svm protein classification. In: Proceedings of the pacific symposium on biocomputing, Hawaii, USA, pp 564-575
39. Lewis DD (1992) An evaluation of phrasal and clustered representations on a text categorization task. In: Proceedings of the annual ACM conference on research and development in information retrieval, Copenhagen, Denmark, pp 37-50
40. H Lodhi C Saunders J Shawe-Taylor N Cristianini C Watkins 2002 Text classification using string kernels J Mach Learn Res 2 1 419 444 1013.68176 10.1162/153244302760200687 (Pubitemid 135712568)
41. Lugaresi N (2004) European union vs. spam: a legal response. In: Proceeding of first conference on email and anti-Spam (CEAS), California, USA
42. Nagamalai C, Dhinakaran D, Lee JK (2007) Multi layer approach to defend ddos attacks caused by spam. In: Proceedings of the international conference on multimedia and ubiquitous engineering, Washington, DC, USA, pp 97-102
43. M Porter 1980 An algorithm for suffix stripping Program 14 3 130 137
44. Rocchio J (1971) Relevance feedback in information retrieval. In: Proceedings of the SMART retrieval system: expriments in automatic document processing, New Jersey, USA, pp 313-323
45. Roy N, McCallum A (2001) Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the eighteenth international conference on machine learning, pp 441-448
46. Rätsch G, Sonnenburg S (2004) Kernel methods in computational biology, MIT Press, chap Accurate Splice Site Detection for Caenorhabditis elegans
47. G Rätsch S Sonnenburg B Schölkopf 2005 Rase: recognition of alternatively spliced exons in c. elegans Bioinformatics 21 1 i369 i377 10.1093/bioinformatics/bti1053 (Pubitemid 41794510)
48. Ruping S (2001) Incremental learning with support vector machines. In: Proceedings of the 2001 IEEE international conference on data mining, Washington, DC, USA, pp 641-642
49. G Salton 1979 Mathematics and information retrival J Doc 35 1 1 29 10.1108/eb026671
50. IJ Schoenberg 1938 Metric spaces and positive definite functions Trans Am Math Soc 44 3 522 536 0019.41502 10.2307/1989894 1501980
51. Schohn G, Cohn D (2000) Less is more: active learning with support vector machines. In: Proceedings of the seventeenth international conference on machine learning, California, USA, pp 839-846
52. Scholkopf B (2000) The kernel trick for distances. In: Proceedings of the advances in neural information processing systems (NIPS), Colorado, USA, pp 301-307
53. Scholkopf B, Smola A (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge, MA
54. Sculley D (2007) Online active learning methods for fast label-efficient spam filtering. In: Proceedings of the fourth conference on email and anti-Spam (CEAS 2007), Berlin, Germany
55. Sculley D, Wachman G (2007) Relaxed online svms for spam filtering. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, Amsterdam, Netherlands, pp 415-422
56. F Sebastiani 2002 Machine learning in automated text categorization ACM Comput Surv 34 1 1 47 10.1145/505282.505283
57. SpamAssassin (2008) http://spamassassin.apache.org/tests
58. SPAMHAUS (2003) The spam definition and legalization game. http://www.spamhaus.org/news.lasso?article=9, Accessed: 31.05.07
59. Szummer M, Jaakkola T (2003) Information regularization with partially labeled data. In: Proceedings of the advances in neural information processing systems (NIPS), British Columbia, Canada
60. Vapnik V (1998) Statistical learning theory. Wiley-Interscience, New York
61. J Wang X Shen 2006 Large margin semi-supervised learning J Mach Learn Res 8 1 1867 1891 2353822
62. Wittel G, Wu S (2004) On attacking statistical spam filters. In: Proceedings of the first conference on email and anti-spam (CEAS), California, USA
63. Xu C, Zhou Y (2007) Transductive support vector machine for personal inboxes spam categorization. In: Proceedings of the international conference on computational intelligence and security workshops, Washington, DC, USA, pp 459-463
64. Zhang D, Sun lee W (2006) Extracting key-substring-group features for text classfication. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, Pennsylvania, USA, pp 474-483
65. Zhu X, Ghahramani Z, Lafferty J (2003) Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the twentieth international conference on machine learning (ICML), Washington, DC, USA, pp 912-919