What question did this study set out to answer?

The aim is to compare traditional machine learning classifiers with a hybrid CNN-LSTM model for detecting phishing emails.

May 14, 2026Open Access

A Comparative Performance Analysis of Machine Learning and Hybrid Deep Learning Models for Phishing Email Detection

Key Points

The aim is to compare traditional machine learning classifiers with a hybrid CNN-LSTM model for detecting phishing emails.
Evaluated models: K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and CNN-LSTM.
Conducted experiments in controlled and heterogeneous scenarios using standard evaluation metrics.
Metrics included accuracy, precision, recall, and F1-score.
The hybrid CNN-LSTM model achieved superior performance compared to traditional machine learning classifiers.
Demonstrated greater robustness in phishing email detection.
Proven suitable for real-time applications in cybersecurity.

Abstract

This paper presents a comparative performance analysis of traditional machine learning classifiers and a hybrid CNN-LSTM deep learning model for phishing email detection. The study evaluates multiple models including K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and the proposed CNN-LSTM architecture. Experiments are conducted under controlled and heterogeneous scenarios using standard evaluation metrics such as accuracy, precision, recall, and F1-score. Results demonstrate that the hybrid CNN-LSTM model consistently achieves superior performance and robustness compared to conventional machine learning approaches, making it suitable for real-time phishing email detection in cybersecurity applications.

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