Classification of Phishing Website Using Hybrid Machine Learning Techniques

The problem with cyber security involves scam websites, stilling the information that exploit people's trust. It could be reduced to the act of enticing internet users even though that they can get their personal data, including user names and passwords. In this study, we present a method for identifying phishing websites. The technology works as an add-on to a web browser, alerting the user when it finds a phishing website. A machine learning technique, specifically supervised learning is proposed in our study. The Logistic regression, Principal Component Analysis (PCA) and Apriori algorithms are chosen because of its success in classification. By examining the characteristics of phishing websites and selecting strongest combination of them, we developed a classifier that performs better