An Optimised Phishing Email Detection and Prevention Using Classification Models

: The act of sending a fake e-mail to a user is known as phishing. It involves imitating a legitimate financial institution or organization in order to trick the recipient into providing their personal information. Due to the harmful effects of phishing emails, the development of classification models that can help identify and prevent fraudulent emails has been considered. Four of the most prominent models in the literature for analyzing phishing emails are K-Nearest Neighbor, Support Vector Machine, Random Forest and Nave Bayes. A model that combines three of the models with better performance metrics using 47 features was developed. It was tested against various existing models and performed well in comparison to them. Finally, the comparison analysis of the model is conducted to archive a realistic accuracy rate of 99 percent.