The website security is an important issue that must be pursued to protect Internet users. Traditionally, blacklists of malicious websites are maintained, but they do not help in the detection of new malicious websites. This work proposes a machine learning architecture for intelligent detecting malicious URLs. Forty-one features of malicious URLs are extracted from the data processes of domain, Alexa and obfuscation. ANOVA (Analysis of Variance) test and XGBoost (eXtreme Gradient Boosting) algorithm are used to identify the 17 most important features. Finally, dataset is used to learn the XGBoost classifier, which has a detection accuracy of more than 99%.