Phishing Website Detection from URLs Using Classical Machine Learning ANN Model

Phishing is a serious form of online fraud made up of spoofed websites that attempt to gain users' sensitive information by tricking them into believing that they are visiting a legitimate site. Phishing attacks can be detected many ways, including a user's awareness of fraud protection, blacklisting websites, analyzing the suspected characteristics, or comparing them to recent attempts that followed similar patterns. The purpose of this paper is to create classification models using features extracted from websites to study and classify phishing websites. In order to train the system, we use two datasets consisting of 58,645 and 88,647 URLs labeled as "Phishing" or "Legitimate". A diverse range of machine learning models such as "XGBOOST, Support Vector Machine (SVM), Random Forest (RF), k-nearest neighbor (KNN), Artificial neural network (ANN), Logistic Regression (LR), Decision tree (DT), and Gaussian naive Bayes (NB)" classifiers are evaluated. ANN provided the best performance with 97.63% accuracy for detecting phishing URLs in experiments. Such a study would be valuable to the scientific community, especially to researchers who work on phishing attack detection and prevention.