Detecting Phishing Websites Using Associative Classification

Abstract

Phishing is a criminal technique employing both social engineering and technical subterfuge to steal consumer's personal identity data and financial account credential. The aim of the phishing website is to steal the victims’ personal information by visiting and surfing a fake webpage that looks like a true one of a legitimate bank or company and asks the victim to enter personal information such as their username, account number, password, credit card number, …,etc. This paper main goal is to investigate the potential use of automated data mining techniques in detecting the complex problem of phishing Websites in order to help all users from being deceived or hacked by stealing their personal information and passwords leading to catastrophic consequences. Experimentations against phishing data sets and using different common associative classification algorithms (MCAR and CBA) and traditional learning approaches have been conducted with reference to classification accuracy. The results show that the MCAR and CBA algorithms outperformed SVM and algorithms.

Keywords: Phishing Websites, Data Mining, Associative Classification, Machine Learning