Phishing is one of the most frequently occurring forms of cybercrime that Internet users face and represents a violation of cybersecurity principles. Phishing is a fraudulent attack that is performed over the Internet with the purpose of obtaining and using without authorization the sensitive information of Internet users, such as usernames, passwords, credit card details, and bank account information. Some widely used phishing attempts involve using email spoofing or instant messaging, aiming to convince a victim to visit the spoofed websites, which will result in obtaining the victim's information. In this work, we identify and analyze the most important features needed to detect the spoofed websites in virtue of two new feature selection techniques. The first proposed feature selection technique uses underlying feature selection methods that vote on each feature, and if such methods agree on a specific feature, that feature is selected. The second feature selection technique also uses underlying feature selection methods that vote on each feature, and if the majority vote on a specific feature, the feature is selected. We also propose a phishing detection technique based on both AdaBoost and LightGBM ensemble methods to detect the spoofed websites. The proposed method achieves a very high accuracy compared to that of the existing methods.
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