Spam email filtering using network-level properties

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6171 LNAI, Issue , 2010, Pages 476-489

  Cortez, Paulo ^a   Correia, André ^a   Sousa, Pedro ^b   Rocha, Miguel ^b   Rio, Miguel ^a  

^a UNIVERSITY OF MINHO   (Portugal)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Anti Spam filtering; Naive Bayes; Support Vector Machines; Text Mining

Indexed keywords

ANTI-SPAM FILTERING; BAG OF WORDS; GEOGRAPHIC COORDINATES; NAIVE BAYES; PHISHING ATTACKS; PORTUGAL; SPAM EMAILS; SVM MODEL; TEXT MINING; TIME SPENT; USER PROFILE;

DATA MINING; ELECTRONIC MAIL; INTERNET; SECURITY OF DATA; VIRUSES;

SUPPORT VECTOR MACHINES;

EID: 77954878926     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14400-4_37     Document Type: Conference Paper

References (17)

1. Beverly, R., Sollins, K.: Exploiting transport-level characteristics of spam. In: 5th Conference on Email and Anti-Spam, CEAS (2008)
2. Bilisoly, R.: Practical text mining with Perl. Wiley Publishing, Chichester (2008)
3. Blanzieri, E., Bryl, A.: A survey of learning-based techniques of email spam filtering. Artificial Intelligence Review 29(1), 63-92 (2008)
4. Cherkassy, V., Ma, Y.: Practical Selection of SVM Parameters and Noise Estimation for SVM Regression. Neural Networks 17(1), 113-126 (2004)
5. Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20(3), 273-297 (1995)
6. Cortez, P., Cerdeira, A., Almeida, F., Matos, T., Reis, J.: Modeling wine p by data mining from physicochemical properties. Decision Support Systems 47(4), 547-553 (2009)
7. Cortez, P., Lopes, C., Sousa, P., Rocha, M., Rio, M.: Symbiotic Data Mining for Personalized Spam Filtering. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI 2009), pp. 149-156. IEEE, Los Alamitos (2009)
8. Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Transactions on Neural networks 10(5), 1048-1054 (1999)
9. Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27, 861-874 (2006)
10. Flexer, A.: 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 (1996)
11. Leiba, B., Ossher, J., Rajan, V.T., Segal, R., Wegman, M.: SMTP path analysis. In: Proceedings of the Second Conference on E-mail and Anti-Spam, CEAS (2005)
12. Lin, H.T., Lin, C.J., Weng, R.C.: A note on Platts probabilistic outputs for support vector machines. Machine Learning 68(3), 267-276 (2007)
13. MAAWG. Email Metrics Program: The Network Operators' Perspective. Report #10 - third and fourth quarter 2008, Messaging Anti-Abuse Working Group, S. Francisco, CA, USA (March 2009)
14. Metsis, V., Androutsopoulos, I., Paliouras, G.: Spam Filtering with Naive Bayes - Which Naive Bayes? In: Third Conference on Email and Anti-Spam, CEAS (2006)
15. Nelson, B., Barreno, M., Chi, F., Joseph, A., Rubinstein, B., Saini, U., Sutton, C., Tygar, J., Xia, K.: Exploiting Machine Learning to Subvert Your Spam Filter. In: 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats, pp. 1-9. ACM Press, New York (2008)
16. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2009), ISBN 3-900051-00-3 http://www.R-project.org
17. Ramachandran, A., Feamster, N.: Understanding the Network-Level Behavior of Spammers. In: ACM (ed.) SIGCOMM 2006, pp. 291-302 (2006)