Phishing Detection using Extra Trees Classifier

Abstract

With a rapid growth in global networking, the online users are vulnerable to different kinds of attacks, phishing being prevalent among them. Phishing is the type of attack where the attacker aims to steal critical information by tricking the user to click on phishing links. There already exists several anti-phishing software and computational methods for actively detecting phishing activities. However, new methods of cybercrimes are evolved by the attackers that surpass the existing detection models. So, there is a constant need to research and improvise the ways to detect phishing. The proposed system develops a web-based application to detect phishing URLs using a machine learning model. Two ensemble classifiers, Random Forest (RF) and Extra Trees (ET) are compared to find the one with higher performance measures. The models are trained on the UCI dataset with 30 features. Hyperparameter Tuning is performed on the models to check whether it enhances their predictive performance. The Extra Trees classifier without tuning achieved the highest accuracy of 97.47% on the test dataset with the least false positive rate.