Email phishing detection based on naïve Bayes, Random Forests, and SVM classifications: A comparative study
Mustafa Al‐Fayoumi, Ammar Odeh, Ismail Keshta, Abobakr Aboshgifa, Tareq Alhajahjeh, Rana Abdulraheem
Abstract
Emails are frequently utilized as a way of personal and professional communication. Banking information, credit reports, login data, and other sensitive personal information are commonly transmitted over email. This makes them valuable to cybercriminals, who can exploit the knowledge for their gain. Phishing is a technique used by con artists to steal sensitive information from people by impersonating well-known sources. The sender of a phished email can persuade you to disclose personal information under pretenses. The detection of a phished email is treated as a classification problem in this research, and this paper shows how machine learning methods are used to categorize emails as phished or not. SVM classifier attains a maximum accuracy of 0.998 percent in email classification.