A CNN–BiLSTM Hybrid Model for Email Phishing Detection

Abstract

Email phishing is by far one of the most common and harmful cybersecurity risks that make use of misguided communication methods to breach sensitive user data. Phishing attacks keep changing, which greatly constrains the performance of conventional rule-based and classical machine-learning methods of detection. The proposed study will introduce a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-term Memory (Bi-LSTM) networks to detect emails with high phishing accuracy. The model proposed examines various elements of email, such as, email headers, subject lines, message bodies and embedded URLs, in the form of a rich feature representation that represents structural and contextual features. The datasets collected in the real world, PhishTank and Mendeley repositories, were preprocessed by feature binning, redundancy reduction as well as class balancing to improve the quality of data and generalization. The experimental findings indicate that CNNBILSTM model has a high accuracy (96), precision (95.9), recall (96) and ROC-AUC (0.99), which is better than a number of baseline models, including Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN) and individual neural networks. The statistical analysis also verifies that the performance improvements are substantial (p < 0.05) and the confidence intervals are not wide which means the study is very consistent and reliable. Based on the findings, it is possible to note that the CNN-BiLSTM model represents a reliable, scalable, and high-performance solution that can be applied to real-time email phishing detection in the contemporary cybersecurity settings.