A Machine-Learning Approach to Phishing Detection and Defense

Abstract

Phishing is a kind of cyber-attack in which perpetrators use spoofed emails and fraudulent web sites to lure unsuspecting online users into giving up personal information. This project looks at the phishing problem holistically by examining various research works and their countermeasures, and how to increase detection. It composes of three studies. In the first study, focus was on dataset gathering, pre-processing, features extraction and dataset division in order to make the dataset suitable for the classification process. In the second study, focus was on metric evaluation of a set of classifiers (C4.5, SVM, KNN and LR) using the accuracy, precision, recall and f-measure metrics. The output of the individual classifier study is used ...