Phishing Email Classification Approach Using Machine Learning Algorithms - A Literature Review

Email phishing is one of the cybersecurity threats that continues to grow, utilizing social engineering to obtain sensitive data. Various machine learning-based approaches have been researched to detect and classify phishing emails. This article presents a literature review of phishing email classification methods, including the K-Nearest Neighbor (KNN) algorithm, Naïve Bayes, Support Vector Machine (SVM), Random Forest, and deep learning-based approaches. The discussion included feature extraction techniques (TF-IDF, Word2Vec, BERT), handling data imbalances, and model performance evaluation. This review identifies current research trends, challenges, and gaps for further research.