Open AccessA Comparative Analysis on Artificial Intelligence Techniques for Web Phishing Classification
Author(s) -
Tengku Balqis binti Tengku Abd Rashid,
Jamaludin Sallim,
Yusnita binti Muhamad Noor
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/769/1/012073
Subject(s) - computer science , phishing , web page , web application security , c4.5 algorithm , classifier (uml) , web intelligence , web content , decision tree , internet security , web mining , machine learning , world wide web , web development , artificial intelligence , support vector machine , the internet , naive bayes classifier , computer security , information security , security service
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.