Phishing Email Classification Approach Using Machine Learning Algorithms - A Literature Review
DOI:
https://doi.org/10.61978/data.v3i3.692Keywords:
Phishing, Email, Machine Learning, Email Classification, K-Nearest Neighbor (KNN), Naïve Bayes, Support Vector Machine (SVM), Random Forest, NLP Feature ExtractionAbstract
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.
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