Phishing website detection and optimization using Modified bat algorithm

This paper presents an approach to overcome the difficulty and complexity in detecting and predicting phishing websites. Existing system is 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 also compared their performances, accuracy, number of rules generated and speed. Even though 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, there is no optimal solution. In proposed system we introduced MBAT a metaheuristic algorithm to get an optimal solution for the search of fake websites. We also compare the proposed algorithm with other existing algorithms, including Ant Colony Optimization and Particle Swarm Optimization.