Deep Learning-Based Phishing Email Detection for Cybersecurity Applications

Phishing emails are one of the most common types of cybersecurity fraud due to human vulnerability, which en­ables sensitive data and security of organizations to be lost. Conventional rule-based and signature-driven intrusion detection mechanisms are not able to keep up with the changing sophisti­cation of phishing attacks. The review of phishing email detection methods based on deep learning, the state-of-the-art architecture including the Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long Short-Term Memory (LSTM) networks, and Transformer based models including the BERT architecture and the Distil BERT one will be used in the current paper. The efficiency of the strategies of deep learning is enormously high as our review of 30 recent publications in the topic indicates an accurateness of 97 to 100 percent. The paper analytically reviews the typical datasets (Enron, Nazario, SpamAssassin), preprocessing techniques, architecture innova­tions and hybrid models and attention mechanisms, and metrics. Our methodology is generalized to combine multilevel feature extraction, transformer-based contextual comprehension, and attention-based mechanisms. It has been demonstrated through the literature of the experiment that hybrid architecture that involves transformers with recurring layers is of the excellent performance, and the types of models like those of improved DistilBERT and BERT LSTM hybrid reflect a high accuracy of 99-100% with a minimal false positive rates. The current review has outlined the challenges that include the imbalance of datasets, adversarial robustness, and domain generalization and offered the future research movement direction to interpretable AI, federated learning, and real-time deployment systems.