Improving phishing email detection performance through deep learning with adaptive optimization

Abstract

Phishing email attacks are becoming increasingly sophisticated, placing a heavy burden on cybersecurity, which requires more advanced detection techniques. Attackers often craft emails that closely resemble those from trusted sources, making it difficult for users and traditional filters to distinguish between legitimate and malicious messages. This paper introduces a new hybrid deep learning and optimizer architecture for detecting phishing emails based on the Mountain Gazelle Optimizer (MGO). A hybrid architecture is proposed, comprising contextual embedding using Bidirectional Encoder Representations from Transformers (BERT), feature extraction with Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) temporal dependencies, and multi-head attention for refining the key feature focus in email text. The dataset used in this paper for phishing detection is obtained from the Kaggle website, which includes phishing and legitimate emails. Hyperparameter optimization with the MGO results in a robust model with good classification accuracy. Our experiments demonstrate improved accuracy, precision, recall, and F1 score, with values of 96.8%, 97.2%, 95.4%, and 96.3%, respectively, for enhanced phishing email detection compared to baseline models. Also, the model reduces false positives by 2.5% compared to state-of-the-art conventional methods. These results demonstrate the effectiveness of transformer-based embeddings, combined with advanced neural networks and optimization techniques, in mitigating phishing threats.