Modelling intelligent phishing detection system for e-banking using Fuzzy Data Mining

2009 International Conference on CyberWorlds, CW '09

Volumn , Issue , 2009, Pages 265-272

  Aburrous, Maher ^a   Hossain, M A ^a   Dahal, Keshav ^a   Thabatah, Fadi ^b  

^a UNIVERSITY OF BRADFORD   (United Kingdom)

^b PHILADELPHIA UNIVERSITY   (Jordan)

Author keywords

Apriori; Association; Classification; Data mining; E banking risk assessment; Fuzzy logic; Phishing

Indexed keywords

APRIORI; ASSOCIATION; ASSOCIATION CLASSIFICATION; E-BANKING; E-BANKING RISK ASSESSMENT; PHISHING;

ASSOCIATIVE PROCESSING; CLASSIFICATION (OF INFORMATION); CRYPTOGRAPHY; FUZZY LOGIC; FUZZY SETS; HUMAN ENGINEERING; NETWORK SECURITY; RISK ASSESSMENT; RISK MANAGEMENT; WORLD WIDE WEB;

DATA MINING;

EID: 72349089708     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CW.2009.43     Document Type: Conference Paper

References (44)

1. WholeSecurity Web Caller-ID, www.wholesecurity.com
2. Anti-Phishing Working Group. Phishing Activity Trends Report, http://antiphishing.org/reports/apwg-report-sep2007-final.pdf. September 2007.
3. B. Adida, S. Hohenberger and R. Rivest, "Lightweight Encryption for Email," USENIX Steps to Reducing Unwanted Traffic on the Internet Workshop (SRUTI), 2005.
4. S. M. Bridges and R. B. Vaughn, "fuzzy data mining and genetic algorithms applied to intrusion detection," Department of Computer Science Mississippi State University, White Paper, 2001.
5. R. Dhamija and J.D. Tygar, "The Battle against Phishing: Dynamic Security Skins," Proc. Symp. Usable Privacy and Security, 2005.
6. FDIC., "Putting an End to Account-Hijacking Identity Theft," http://www.fdic.gov/consumers/consumer/idtheftstudy/identity-theft.pdf, 2004.
7. A. Y. Fu, L. Wenyin and X. Deng, "Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD)," IEEE transactions on dependable and secure computing, vol. 3, no. 4, 2006.
8. A. Herzberg and A. Gbara, "Protecting Naive Web Users," Draft of July 18, 2004.
9. C. Y. Ho, B. W. Ling and J. D. Reiss, "Fuzzy Impulsive Control of High-Order Interpolative Low-Pass Sigma-Delta Modulators," IEEE Transactions on Circuits and Systems-I: Regular Papers, Vol. 53, No. 10, October 2006.
10. L. James, "Phishing Exposed," Tech Target Article sponsored by: Sunbelt software, searchexchange.com, 2006.
11. M. Liu, D. Chen and C. Wu., "The continuity of Mamdani method," International Conference on Machine Learning and Cybernetics, Page(s): 1680-1682 vol.3, 2002.
12. W. Liu, G. Huang, X. Liu, M. Zhang, and X. Deng, "Phishing Web Page Detection," Proc. Eighth Int'l Conf. Documents Analysis and Recognition, pp. 560-564, 2005.
13. W. Liu, X. Deng, G. Huang and A. Y. Fu, "An Antiphishing Strategy Based on Visual Similarity Assessment," Published by the IEEE Computer Society 1089-7801/06 IEEE, INTERNET COMPUTING IEEE, 2006.
14. Microsoft Corp, "Microsoft Phishing Filter: A New Approach to Building Trust in E-Commerce Content," White Paper, 2005.
15. S. Olsen, "AOL tests caller ID for e-mail," CNET News.com, January 22, 2004.
16. Y. Pan and X. Ding, "Anomaly BasedWeb Phishing Page Detection," Proceedings of the 22nd Annual Computer Security Applications Conference (ACSAC'06), Computer Society, 2006.
17. J. C. Perez, "Yahoo airs antispam initiative," ComputerWeekly.com, December 8, 2003.
18. S. Shah, "Measuring Operational Risks using Fuzzy Logic Modeling, "Article, Towers Perrin, JULY 2003.
19. T. Sharif, "Phishing Filter in IE7," http://blogs.msdn.com/ie/ archive/2005/09/09/463204.aspx, September 9, 2006.
20. L. Wood, "Document Object Model Level 1 Specification," http://www.w3.org, 2005.
21. M. Wu, R. C. Miller and S. L. Garfinkel, "Do Security Toolbars Actually Prevent Phishing Attacks?," CHI April 2006.
22. M. Wu, R. C. Miller and G. Little, "Web Wallet: Preventing Phishing Attacks by Revealing User Intentions," MIT Computer Science and Artificial Intelligence Lab, 2006.
23. Anders Persson, "Exploring Phishing Attacks and Countermeasures", Master Thesis in Computer Science, Thesis No: MCS-2007:18, September 2007.
24. Rachna Dhamija, J. D. Tygar and Marti Hearst, "Why Phishing Works", Harvard University, White Paper, 2006.
25. "Putting an end to account-hijacking identity theft," FDIC, Tech. Rep., Dec. 2004. [Online]. Available: http://www.fdic.gov/consumers/ consumer/idtheftstudy/identitytheft.pdf.
26. Ian Fette, Norman Sadeh and Anthony Tomasic, "Learning to Detect Phishing Emails", Institute for Software Research International, CMU-ISRI-06-112, June 2006.
27. WEKA - University of Waikato, New Zealand, EN, 2006: "Weka - Data Mining with Open Source Machine Learning Software in Java"; http://www.cs.waikato.ac.nz/ml/weka (2006/01/31).
28. Sebastian Misch, "Content Negotiatio in Internet Mail", Diploma Thesis, University of Applied Sciences Cologne, Mat.No.: 7042524, February 2006.
29. Vic Ciesielski and Anand Lalani, "Data Mining of Web Access Logs From an Academic Web Site", Proceedings of the Third International Conference on Hybrid Intelligent Systems (HIS'03): Design and Application of Hybrid Intelligent Systems, Pages 1034-1043, December 2003, IOS Press.
30. Lyman, Peter; Hal R. Varian (2003). "How Much Information". http://www.sims.berkeley.edu/how-much-info-2003. Retrieved on 2008-12-17.
31. Kantardzic and Mehmed. "Data Mining: Concepts, Models, Methods, and Algorithms.", John Wiley & Sons. ISBN 0471228524. OCLC 50055336, 2003.
32. U.M. Fayyad, "Mining Databases: Towards Algorithms for Knowledge Discovery," Data Eng. Bull., vol. 21, no. 1, pp. 39-48, 1998.
33. Bing Liu, Wynne Hsu, Yiming Ma, "Integrating Classification and Association Rule Mining." Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98, Plenary Presentation), New York, USA, 1998.
34. I.H. Witten, E. Frank, Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco, CA, 2005.
35. J. Cendrowska. "PRISM: An algorithm for inducing modular rule", International Journal of Man-Machine Studies (1987), Vol.27, No.4, pp.349-370.
36. J. R. Quinlan, "Improved use of continuous attributes in c4.5", Journal of Artificial Intelligence Research, 4:77-90, 1996.
37. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules in large databases", Proc International Conference on Very Large Databases, pp. 478-499. Santiage, Chile: Morgan Kaufmann, Los Altos, CA, 1994.
38. T. Scheffer, "Finding Association Rules That Trade Support Optimally against Confidence", Proc of the 5th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), pp. 424-435, Germany: Springer-Verlag, (2001).
39. http://www.phishtank.com/phish-archive.php
40. E. Han and G. Karypis, "Centroid-Based Document Classification: Analysis and Experimental Results, Principles of Data Mining and Knowledge Discovery", p. 424-431, 2000.
41. Fadi T.,Peter C. & Peng Y, "MCAR: Multi-class Classification based on Association Rule", IEEE International Conference on Computer Systems and Applications, 2005, pp. 127-133.
42. F.Thabtah, P. Cowling and Y. Peng, "A new multi-class,multi-label associative classification approach". The 4th International Conference on Data Mining(ICDM'04), Brighton, UK, 2004.
43. Microsoft, "http://www.microsoft.com/mscorp/twc/privacy/spam", April 2004.
44. Netcraft, "http://toolbar.netcraft.com/", Dec 2004.