Machine Learning and Deep Learning for Phishing Email Classification using One-Hot Encoding

Abstract

Representation of text is a significant task in Natural Language Processing (NLP) and in recent years Deep Learning (DL) and Machine Learning (ML) have been widely used in various NLP tasks like topic classification, sentiment analysis and language translation. Until very recently, little work has been devoted to semantic analysis in phishing detection or phishing email detection. The novelty of this study is in using deep semantic analysis to capture inherent characteristics of the text body. One-hot encoding was used with DL and ML techniques to classify emails as phishing or non-phishing. A comparison of various parameters and hyperparameters was performed for DL. The results of various ML models, Naïve Bayes, SVM, Decision Tree, as well as DL models, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM), were presented. The DL models performed better than the ML models in terms of accuracy, but the ML models performed better than the DL models in terms of computation time. CNN with Word Embedding performed the best in terms of accuracy (96.34%), demonstrating the effectiveness of semantic analysis in phishing email detection.