The boosting of financial crimes is a serious and pervasive issue in recent years. Phishing is a criminal act in which attackers seek to obtain important information of a person in the form of username, password and other sensitive information by means of fake and malicious URLs. An important way to deal with these crimes is to detect phishing. Phishing detection is a process that aims to find and detect illegal attempts to steal confidential information and login. Phishing detection has various methods. One of the most important and best methods is to use machine learning. In recent years, the issue of detecting phishing has been seriously pursued in Iraq. For this purpose, in this research, a phishing detection scheme on websites based on the hierarchical machine learning method, presenting two levels in two levels, Simple detection and Complex detection, has been presented. DTC algorithm is used for the first level and SVM-NB-LR is used for the second level. Using this hierarchical model can automatically identify phishing pages with high accuracy and warn users. The simulation results show that the proposed scheme is both fast and accurate and intelligently detects phishing with an accuracy of 98.1.
