Deep Learning-Based Phishing Email Detection for Cybersecurity Applications
1st Rokam Likhita 2nd Ponnamanda Lahari 3rd Gunda Joshith Kumar
Dept. of Computer Application,Aditya University,Surampalem, India
rokamlikhitha@gmail.com ponnamandalahari966@gmail.com joshithurs@gmail.com
4th B N.V Sai Durga 5th Peddinti Rajesh
Dept. of Computer Application,Aditya University,Surampalem, India
saidurgab5@gmail.com rajeshpeddinti98@gmail.com
Abstract—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.
Keywords: Phishing Detection, Deep Learning, Cybersecurity, BERT, LSTM, CNN, Transformer Models, Email Security, Natural Language Processing, Attention Mechanism
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