Phishing attack is the simplest way to obtain sensitive information from innocent users. The target of phishers is to obtain key information, such as username, password, and bank account details. Cyber security officials are now looking for reliable and stable detection techniques to detect phishing sites. This paper studies the phishing websites detection technology by extracting and analyzing the characteristics of legal and forged Uniform Resource Locators (URLs) using machine learning approaches, including Logistic Regression, K-nearest Neighbor (KNN), Linear Support Vector Classifier (SVC), Random Forest, Gradient Boosting Decision Tree, and Ada-Boost, and compares their performance with respect to criterions such as accuracy, Root Mean Square Error (RMSE), precision, recall, and F1-score. The results show that ensemble methods, including Gradient Boosting Decision Tree, Random Forest, and Ada Boosting, can achieve much higher detection performance than the other algorithms in terms of all the criteria.
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