Intelligent Association Classification Technique for Phishing Website Detection

Abstract

Many critical applications need more accuracy and speed in the decision making process. Data mining scholars developed set of artificial automated tools to enhance the entire decisions based on type of application. Phishing is one of the most critical application needs for high accuracy and speed in decision making when a malicious webpage impersonates as legitimate webpage to acquire secret information from the user. In this paper, we proposed a new Association Classification (AC) algorithm as an artificial automated tool to increase the accuracy level of the classification process that aims to discover any malicious webpage. An Intelligent Association Classification (IAC) algorithm developed in this article by employing the Harmonic Mean measure instead of the support and confidence measure to solve the estimation problem in these measures and discovering hidden pattern not generated by the existing AC algorithms. Our algorithm compared with four well-known AC algorithm in terms of accuracy, F1, Precision, Recall and execution time. The experiments and the visualization process show that the IAC algorithm outperformed the others in all cases and emphasize on the importance of the general and specific rules in the classification process.