Main Article Content
Phishing website is the process of enticing people to visit fraudulent e-banking websites and persuading them to
enter identity information such as user names and passwords. This paper presents a novel approach to overcome the
difficulty and complexity in detecting and predicting e-banking phishing websites. The proposed system is an intelligent
resilient and effective model that is based on using association and classification Data Mining algorithms combining with
Ant Colony Optimization technique. These classification 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. The Ant colony
optimization algorithm implemented to detect e-banking phishing websites. The experimental results demonstrated the
feasibility of using Associative Classification technique and Ant Colony Optimization in real applications and its better
Key words: Association and Classification, Ant Colony Optimization, Fuzzification, Defuzzification.
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