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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 12    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 12, December 2011
An Effective Strategy for Identifying Phishing Websites using Class-Based Approach
Full Text(PDF, 3000)  PP.  
Author(s)
K. Ruth Ramya, K. Priyanka, K. Anusha, Ch. Jyosthna Devi, Y. A. Siva Prasad
KEYWORDS
Association rule, Classification , Data Mining, Data Set,Phishing ,Pruning, Rule Mining
ABSTRACT
This paper presents a novel approach to overcome the difficulty and complexity in detecting and predicting social networking phishing website. We proposed an intelligent resilient and effective model that is based on using A New Class Based Associative Classification Algorithm which is an advanced and efficient approach than all other association and classification Data Mining algorithms. This algorithm is used to characterize and identify all the factors and rules in order to classify the phishing website and the relationship that correlate them with each other. Applying the association rule into classification can improve the accuracy and obtain some valuable rules and information that cannot be captured by other classification approaches. The class label is taken good advantage in the rule mining step so as to cut down the searching space. The proposed algorithm also synchronize the rule generation and classifier building phases, shrinking the rule mining space when building the classifier to help speed up the rule generation.
References
[1]T., Fadi, c.Peter and Y. Peng, ""MCAR: Multi-class Classification based on Association Rule"", IEEE International Conference on Computer Systems and Applications ,2005, pp. 127-133.

[2]G. Chen, H. Liu et al, “A new approach to classification based on association rule mining”, Decision Support Systems 42, 2006, pp.674-689.

[3] T. Sharif, “Phishing Filter in IE7,” http://blogs.msdn.com/ie/archive/2005/463204.a spx, , 2006

[4]T. Moore and R. Clayton, ""An empirical analysis of the current state of phishing attack and defence"", In Proceedings of the Workshop on the Economics of Information Security (WEIS2007)

[5]Zhonghua Tang and Qin Liao""A New Class Based Associative Classification Algorithm""IJAM 2007

[6] Anti-Phishing Working Group. Trends Report , http v/antiphishing.org/apwgrcport_sep_ final.pdf2007 .

[7]http://www.darkreading.com/security/attacks-breaches/218101868/index.html

[8]https://www.markmonitor.com/pr/brandjacking/spring2009/

[9] www.onlinesbi.com retrieved on 26 August,2010

[10] www.sbionline.com retrieved on 26 August,2010

[11] Maher Aburrous, M.A. Hossain, Keshav Dahal, Fadi Thabtah “Associative Classification Techniques for predicting e-Banking Phishing Websites” IEEE 2010

[12]A.Martin, Na.Ba.Anutthamaa, M.Sathyavathy, Marie Manjari Saint Francois,Dr.Prasanna Venkatesan ""A Framework for Predicting Phishing Websites UsingNeural Networks ""IJCSI 2011

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