Associative classification techniques for predicting e-banking phishing websites

Maher Aburrous, Alamgir Hossain, Keshav Dahal, Fadi Thabtah

    Research output: Contribution to conferencePaperpeer-review

    15 Citations (Scopus)

    Abstract

    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.
    Original languageEnglish
    DOIs
    Publication statusPublished - Mar 2010
    Event2010 International conference on Multimedia computing and Information Technology (MCIT) - Sharjah University, United Arab Emirates
    Duration: 1 Mar 2010 → …

    Conference

    Conference2010 International conference on Multimedia computing and Information Technology (MCIT)
    Period1/03/10 → …

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