Abstract

Phishing is a fraudulent act carried out to try to get important information from users who use the internet by sending fake e-mails to the users. Data mining classification techniques can be used to predict phishing websites. Many data mining classification algorithms can be used, so it is necessary to make comparisons to determine the level of accuracy of each algorithm. The algorithm used is naïve Bayes, random forest, decision tree, and support vector machine. The data used are 1.353 websites data. The results of the classification process are evaluated using cross validation and confusion matrix to find out the most accurate data mining classification algorithms for predicting phishing websites.