Comparative study of data mining classification techniques for detection and prediction of phishing websites

Abstract

Data mining is the process of discovering or extracting information from large amount of data that are stored in databases or datasets such as phishing dataset. Phishing is a vital web security problem that involves simulating legitimate websites to mislead online users in order to steal their sensitive information. This paper aims to detect and predict the type of the website to either legitimate or phishing class label. It investigates different data mining classifiers that are applied to the phishing dataset aiming to determine the effective ones in terms of classification performance. The comparison between nine classifiers with help of rapid miner software was conducted. Here, for comparing the result, five different metrics were used including accuracy, precision, recall, sensitivity and FMeasure. In this study, it has been able to identify the classifiers that precisely recognize fake websites especially with respect to the evolutionary nature of the information attacks.