Detection of Phishing Websites Using Machine Learning Approach

Abstract

Phishing attacks are growing in the similar manner as e-commerce industries are growing. Prediction and prevention of phishing attacks is a very critical step towards safeguarding online transactions. Data mining tools can be applied in this regard as the technique is very easy and can mine millions of information within seconds and deliver accurate results. With the help of machine learning algorithms like, Random Forest, Decision Tree, Neural network and Linear model we can classify data into phishing, suspicious and legitimate. This can be done based on unique features of phishing websites and user does not need to check individual websites. Rather we can identify and predict phishing, suspicious and legitimate websites by extracting some unique features. The aim of this work was to develop model to safeguard users from phishing attack. In this paper Random Forest, Decision Tree, Linear model and Neural Network algorithms have been used on a phishing dataset. The results of these algorithms have then been compared in terms of accuracy, error rate, precision, and recall.