Phishing Email Detection Using Machine Learning: A Critical Review
Gunjan, Rajesh Prasad
Abstract
Phishing is a prominent form of cybercrime that involve deceiving victims into divulging personal information such as bank account details, passwords, and account IDs. Cyberattacks using emails, instant messaging, and phone conversations continue even if defense mechanisms are being improved. Phishing is a scam tactic that uses email correspondence to get private information by impersonating reliable sources. According to experimental results, the best classifiers for identifying email phishing assaults are SVM, NB, and LSTM, with accuracy rates of 99.62%, 97%, and 98%, respectively. Phishing assaults mostly target email, which emphasizes the importance of having a thorough understanding of the always changing research scene in order to identify these fraudulent actions. This review distinguishes itself by carefully reviewing latest academic articles published between 2006 and 2022 stating their pros and cons.