Phishing website detection using latent dirichlet allocation and AdaBoost

ISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics: Cyberspace, Border, and Immigration Securities

Volumn , Issue , 2012, Pages 102-107

  Ramanathan, Venkatesh ^a   Wechsler, Harry ^a  

^a George Mason University   (United States)

Author keywords

boosting; detection; identity theft; machine learning; natural language processing; phishing website; semantic analysis

Indexed keywords

BOOSTING; IDENTITY THEFT; NATURAL LANGUAGE PROCESSING; PHISHING; SEMANTIC ANALYSIS;

ADAPTIVE BOOSTING; COMPUTER CRIME; COMPUTERS; CRIME; DIGITAL STORAGE; ERROR DETECTION; HYPERTEXT SYSTEMS; INFORMATION DISSEMINATION; INFORMATION SCIENCE; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; PROBABILITY DISTRIBUTIONS; SEMANTICS; STATISTICS;

WEBSITES;

EID: 84867339625     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISI.2012.6284100     Document Type: Conference Paper

References (31)

1. Phishing Activity Trends Report, http://www.antiphishing.org/ phishReportsArchive.html, Last accessed Feb 20, 2012.
2. Mobile Commerce Daily, http://www.mobilecommercedaily.com/2011/11/28/how- consumers-will-use-their-mobile-devices-during-the-holidays, Last accessed Feb 20, 2012.
3. Trusteer, http://www.trusteer.com/blog/mobile-users-three-times-more- vulnerable-phishing-attacks, Last accessed February 20, 2012.
4. M. Aburrous, M. A. Hossain, K. Dahal and F. Thabtah, "Associative classification techniques for predicting e-Banking phishing websites", Internaional Conference on Multimedia Computing and Information Technology, Sharjah, 2010.
5. L. Wenyin, G. Liu, B. Qiu and X. Quan, "Anti-phishing by discovering phishing target", IEEE Internet Computing, Issue 99, 2011.
6. M. He, S. J. Horng, P. Fan, M. K. Khan, R. S. Run, J. L. Lai, R. J. Chen and A. Sutanto, "An efficient phishing webpage detector", Expert Systems with Applications, Elsevier, Vol. 38, 2011.
7. G. Xiang, J. Hong, C. P. Rose and L. Cranor, "A feature-rich machine learning framework for detecting phishing websites", ACM Transaction on Information Systems Security, Vol. 14, Article 21, September, 2011.
8. M. Khonji, A. Jones and Y. Iraqi, "A novel phishing classification based on URL features", IEEE GCC Conference and Exhibition, Dubai, 2011.
9. H. Zhang, G. Liu, T. W. S. Chow and W Liu, "Textual and visual content-based anti phishing: A Bayesian Approach, IEEE Transactions on Neural Networks, Vol. 22, No. 10, 2011.
10. C. H. Hsu, P. Wang and S. Pu, "Identify fixed path phishing attack by STC", Proceedings of 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, Perth, Australia, September 2011.
11. A P. Felt and D. Wagner, "Phishing on mobile devices," Web 2.0 Security & Privacy 2011, Oakland, Calfornia, May, 2011.
12. Tor Project: Anonymity Online, Last accessed Feb 20, 2011.
13. D. M. Blei, A. Y. Ng, M. I. Jordan, "Latent Dirichlet Allocation", Journal of Machine Learning Research, Vol. 3, pp. 993-1022, 2003.
14. T. Hoffman, "Probablilistic latent semantic indexing", Proceedings of the Twenty-Second Annual International SIGIR Conference, 1999.
15. th Conference in Uncertainity in Artificial Intelligence, 2002.
16. T. L. Griffiths and M. Steyvers, "Finding scientific topics", Proceedings of the National Academy of Sciences of the USA, 101 (Suppl. 1), pp 5228-5235, 2004.
17. Stanford TMT, http://nlp.stanford.edu/software/tmt/tmt-0.4/, Last accessed Feb 20, 2012.
18. Y. Freund and R. Schapire, "A short introduction to boosting", Journal of Japanese Society for Artificial Intelligence, pp. 115-123, 1995.
19. R. Quinlan, C4.5: Programs for machine learning, Morgan Kauffmann Publishers, San Mateo, CA, 1993.
20. th International Conference on Machine Learning, pp. 115-123, 1995.
21. L. Breiman, Random Forests, Machine Learning, 45(1), pp. 5-32, 2001.
22. V. Vapnik, The nature of statistical learning theory, Springer, New York, 1995.
23. th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 675-683, 2005.
24. M. D. Buhmann, Radial basis functions: theor and implementattions, Cabridge University, 2003.
25. th Annual Conference on Computational Learning Theory, New York, pp. 209-217, 1998.
26. th Conference on Uncertainty in Artificial Intelligence, San Mateo, pp. 338-345, 1995.
27. DMOZ, http://www.dmoz.org/, Last accessed Feb 20, 2012.
28. Alexa, http://www.alexa.com/, Last accessed Feb 20, 2012.
29. Phishtank, http://www.phishtank.com/, Last accessed Feb 20, 2012.
30. Tesseract, http://code.google.com/p/tesseract-ocr/, Last accesed Feb 20, 2012.
31. Google Labs, http://code.google.com/labs/, Last accesed Feb 20, 2012.