Comparative Analysis of Machine Learning Algorithms for Text-based Phishing Detection

Abstract: Phishing is a major threat to personal and organizational security. The risks associated with phishing have increased as cyber criminals devise more convincing methods to deceive their targets. Deceptive emails can often result in the compromise of personal information, accounts, financial loss and reputational damage. This paper explores how certain commonly used machine learning algorithms perform in detecting malicious emails based on the text alone. The algorithms analyzed are Support Vector Machines (SVM), Naive Bayes (NB) and Logistic Regression (LR). The paper compares the performance of these algorithms based on various factors such as accuracy, precision and recall. It also compares the performance of a voting approach with the algorithms.