Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Mohammad, Rami; McCluskey, T.L.; Thabtah, Fadi Abdeljaber (2014)
Publisher: Institution of Engineering and Technology
Languages: English
Types: Article
Subjects: TN

Classified by OpenAIRE into

Phishing is described as the art of emulating a website of a creditable firm intending to grab user’s private information such as usernames, passwords and social security number. Phishing websites comprise a variety of cues within its content-parts as well as browser-based security indicators. Several solutions have been proposed to tackle phishing. Nevertheless, there is no single magic bullet that can solve this threat radically. One of the promising techniques that can be used in predicting phishing attacks is based on data mining. Particularly the “induction of classification rules”, since anti-phishing solutions aim to predict the website type accurately and these exactly fit the classification data mining. In this paper, we shed light on the important features that distinguish phishing websites from legitimate ones and assess how rule-based classification data mining techniques are applicable in predicting phishing websites. We also experimentally show the ideal rule based classification technique for detecting phishing.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 2. Sophie GP, Gustavo GG, Maryline L. Decisive Heuristics to Differentiate Legitimate from Phishing Sites. In 2011 Conference on Network and Information Systems Security; 2011: IEEE. p. 1-9.
    • 3. Guang X, Jason o, Carolyn P R, Lorrie C. CANTINA+: A Feature-rich Machine Learning Framework for Detecting Phishing Web Sites. ACM Transactions on Information and System Security. 2011 Sep: p. 1-28.
    • 4. Witten IH, Frank E. Data mining: practical machine learning tools and techniques with Java implementations. New York, NY, USA:; March 2002.
    • 5. H DJ. Rule induction-machine learning techniques. Computing & Control Engineering Journal. 1994 October: p. 249-255.
    • 6. Gartner, Inc. [Online]. Available from: http://www.gartner.com/technology/home.jsp.
    • 7. Lennon, M. Security Week. [Online].; 2011. Available from: http://www.securityweek.com/cisco-targeted-attacks-costorganizations-129-billion-annually.
    • 8. Aburrous, M , Hossain, M. A. , Dahal, K. , Fadi, T. Predicting Phishing Websites using Classification Mining Techniques. In Seventh International Conference on Information Technology.; 2010; Las Vegas, Nevada, USA.: IEEE. p. 176-181.
    • 9. Thabtah F, Peter C, Peng Y. MCAR: Multi-class Classification based on Association Rule. In The 3rd ACS/IEEE International Conference on Computer Systems and Applications; 2005. p. 33.
    • 10. Hu K, Lu Y, Zhou L, Shi C. Integrating Classification and association rule Mining. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98, Plenary Presentation); 1998; New York, USA: Springer-Verlag. p. 443 - 447.
    • 11. Quinlan JR. Improved use of continuous attributes in c4.5. Journal of Artificial Intelligence Research. 1996;: p. 77-90.
    • 12. Cendrowska J. PRISM: An algorithm for inducing modular rule. International Journal of Man-Machine Studies. 1987;: p. 349- 370.
    • 13. Aburrous , Hossain MA, Dahal K, Thabtah F. Intelligent phishing detection system for e-banking using fuzzy data mining. Expert Systems with Applications: An International Journal. 2010 December: p. 7913-7921.
    • 14. Pan , Ding. Anomaly Based Web Phishing Page Detection. In In ACSAC '06: Proceedings of the 22nd Annual Computer Security Applications Conference.; Dec. 2006: IEEE. p. 381-392.
    • 15. Cortes C, Vapnik V. Support-Vector Networks. Machine Learning. Sept.1995;: p. 273 - 297.
    • 16. Zhang , Hong , Cranor. CANTINA: A Content-Based Approach to Detect Phishing Web Sites. In Proceedings of the 16th World Wide Web Conference; May, 2007.
    • 17. Manning C, Raghavan , Schütze H. Introduction to Information Retrieval: Cambridge University Press; 2008.
    • 18. Sadeh N, Tomasic A, Fette I. Learning to detect phishing emails. Proceedings of the 16th international conference on World Wide Web. 2007: p. 649-656.
    • 19. PhishTank. [Online].; 2006 [cited 2011 November 25. Available from: http://www.phishtank.com/.
    • 20. millersmiles. millersmiles. [Online].; 2011 [cited 2011 October. Available from: http://www.millersmiles.co.uk/.
    • 21. Horng SJ, Fan P, Khan MK, Run RS, Lai JL, Chen RJ, et al. An efficient phishing webpage detector. Expert Systems with Applications: An International Journal. 2011; 38 (10): p. 12018-12027.
    • 22. More than 450 Phishing Attacks Used SSL in 2005. [Online]. [Cited 2012 March 8. Available from: http://news.netcraft.com/archives/2005/12/28/more_than_450_phishing_attacks_used_ssl_in_2005.html.
    • 23. WhoIS. [Online]. Available from: http://who.is/.
    • 24. Rasmussen R, Aaron G. Global Phishing Survey: Trends and Domain Name Use 2H2009 [Survey]. Lexington; 2010. Available from: http://anti-phishing.org/reports/APWG_GlobalPhishingSurvey_2H2009.pdf.
    • 25. Ask Sucuri. Security Blog. [Online].; 2011. Available from: http://blog.sucuri.net/2011/12/ask-sucuri-how-long-it-takes-for-asite-to-be-removed-from-googles-blacklist-updated.html.
    • 26. Alexa the Web Information Company. [Online]. [Cited 2012 January 26. Available from: http://www.alexa.com/.
    • 27. Yahoo Directory. [Online]. Available from: http://dir.yahoo.com/.
    • 28. Starting Point Directory. [Online]. Available from: http://www.stpt.com/directory/.
    • 29. Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules. VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases. 1994;: p. 487-499.
    • 30. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. Waikato Environment for Knowledge Analysis. [Online]. Available from: http://www.cs.waikato.ac.nz/ml/weka/.
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article