Feature-based performance comparison of machine learning algorithms for phishing detection through uniform resource locator

Recently, phishing attacks are very common. Such attacks are carried out with the aim of obtaining personal information of individuals or defrauding individuals. There are multiple types of phishing attacks. One of these types is the common attacks carried out through the uniform resource locator (URL). The purpose of this study is to classify whether URL addresses are malicious or not using different machine learning algorithms. Eight different machine learning algorithms including support vector machines, random forest, Gaussian Naive Bayes, logistic regression, k-nearest neighbor, decision trees, multilayer perceptrons and XGBoost algorithms were used in the study. Data were obtained from USOM, Alexa, and Phishtank to be used for training and testing purposes. Feature extraction was performed limited by applying various data pre-processing steps to these data. As a result of the research, the accuracy of 99.8% in more than one model has been achieved, and the success of machine learning algorithms in this area has been proven.