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September 5, 2025

A Survey on Phishing Email Detection Techniques: Using LSTM and Deep Learning

Key Points

Deep learning techniques boost phishing email detection effectiveness amid evolving threats.
Key methods include lstm, convolutional neural networks, and hybrid architectures for analysis.
Evaluation metrics and feature extraction techniques improve detection systems' reliability.
Commonly used datasets provide essential benchmarks for validating detection model performance.

Abstract

Phishing attacks, often delivered through deceptive emails, remain one of the most dangerous cyber threats, aiming to steal sensitive information such as passwords and financial data. Traditional detection methods like blacklists and rule-based filters struggle to keep up with evolving tactics. This paper surveys recent deep learning approaches to phishing email detection, focusing on models such as Long Short-Term Memory (LSTM), Convolutional Neural Network(CNN), Bidirectional Long Short-Term Memory (BiLSTM), Transformer encoders, and hybrid architectures. These methods analyze email components like subject lines, content, metadata, and user behavior. The study also reviews commonly used datasets, feature extraction techniques, and evaluation metrics, providing insights into current trends, strengths, and challenges in developing effective phishing detection systems.

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