A Comparative Analysis on Artificial Intelligence Techniques for Web Phishing Classification

Abstract

Over the last few years, the web has been expanded to serve millions of users for various purposes all over the world. The web content filtering is essential to filter offensive, unwanted web content from web pages, reduced inappropriate content to prevent access to content which could compromise the network and spread malware. It also to tightened network security where web content filtering adds a much-needed layer of security to the network by blocking access to sites that raise an alarm. However, there are lack of comparison between classification techniques in previous studies in order to find the best classifier for the web page classification and the analysis related to it. Thus, the purpose of this study was to apply web page classification techniques and their performances is compared as it is the initial step in data mining before going to web filtering. In this project, three classifiers called Artificial Neural Network, J48 Decision Tree and Support Vector Machine were used to web phishing dataset in order to find the best possible classifier with small computational efforts that will give the best result in classifying the web page.