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Fake Website, Association and Classification Technique, ACO

Fake websites is the process of attracting people to visit fraudulent websites and making them to enter confidential data like credit-card numbers, usernames and passwords. We present a novel approach to overcome the difficulty and complexity in detecting and predicting fake website. There is an efficient model which is based on using Association and classification Data Mining algorithms combining with ACO algorithm. 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. It also used PART classification algorithm to extract the phishing training data sets criteria to classify their legitimacy. But, this work has limitations like Sequences of random decisions (not independent) and Time to convergence uncertain in the phishing classification. So to overcome this limitation we enhance Particle Swarm Optimization (PSO) which finds a solution to an optimization problem in a search space, or model and predict social behavior in the presence of phishing websites. This will improve the correctly classified phishing websites. The experimental results demonstrated the feasibility of using PSO technique in real applications and its better performance. This project employs the JAVA technology.