Machine Learning Approach for Email Phishing Detection

Abstract

The widespread usage of the Internet in numerous everyday life applications has resulted in an increase in the number of phishing assaults. Phishing attacks exist in a variety of forms and have gotten increasingly sophisticated which, making traditional security measures no longer efficient. According to many researches. Methods based on artificial intelligence especially, Machine Learning (ML), and Deep Learning (DL) have proven to be an efficient solution of cyber Security. The aim of this study paper is to propose an efficient and accurate approach for enhancing phishing emails detection, based on Learning model and features selection technique to extract only the significant features. For that reason, different computational models were developed including Random Forest, SVM, and XGBoost and were tested rigorously using a specialized dataset. The experimental results revealed that the SVM model achieved the highest scores in all validation tests, with an accuracy of 0.976120, followed by the XGBoost with an accuracy of 0,966192. This demonstrates that our approach can enhance the accuracy rate with a minimum time of execution and that our model is robust and accurate in terms of email phishing detection.