Associative classification techniques for predicting e-banking phishing websites

Aburrous, Maher, Hossain, Alamgir, Dahal, Keshav and Thabtah, Fadi (2010) Associative classification techniques for predicting e-banking phishing websites. In: 2010 International conference on Multimedia computing and Information Technology (MCIT), 2-4 March 2010, Sharjah University, United Arab Emirates.

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This paper presents a novel approach to overcome the difficulty and complexity in detecting and predicting e-banking phishing website. We proposed an intelligent resilient and effective model that is based on using association and classification Data Mining algorithms. These algorithms were 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. We implemented six different classification algorithm and techniques to extract the phishing training data sets criteria to classify their legitimacy. We also compared their performances, accuracy, number of rules generated and speed. The rules generated from the associative classification model showed the relationship between some important characteristics like URL and Domain Identity, and Security and Encryption criteria in the final phishing detection rate. The experimental results demonstrated the feasibility of using Associative Classification techniques in real applications and its better performance as compared to other traditional classifications algorithms.

Item Type: Conference or Workshop Item (Paper)
Subjects: G400 Computer Science
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
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Depositing User: EPrint Services
Date Deposited: 08 Sep 2011 11:29
Last Modified: 13 Oct 2019 00:30

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