Predicting phishing websites using classification mining techniques with experimental case studies

Aburrous, Maher, Hossain, Alamgir, Dahal, Keshav and Thabtah, Fadi (2010) Predicting phishing websites using classification mining techniques with experimental case studies. In: 7th International conference on Information Technology: New generations (ITNG), 12-14 April 210, Las Vegas, NV, USA.

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Classification Data Mining (DM) Techniques can be a very useful tool in detecting and identifying e-banking phishing websites. In this paper, we present 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. A Phishing Case study was applied to illustrate the website phishing process. 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)
Uncontrolled Keywords: association , data mining , fuzzy logic , machine learning
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
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Depositing User: EPrint Services
Date Deposited: 02 Sep 2011 16:10
Last Modified: 13 Oct 2019 00:30

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