Context-Aware Spam Detection Using BERT Embeddings with Multi-Window CNNs

Spam emails remain one of the most persistent threats to digital communication, necessitating effective detection solutions that safeguard both individuals and organisations. We propose a spam email classification framework that uses Bidirectional Encoder Representations from Transformers (BERT) for contextual feature extraction and a multiple-window Convolutional Neural Network (CNN) for classification. To identify semantic nuances in email content, BERT embeddings are used, and CNN filters extract discriminative n-gram patterns at various levels of detail, enabling accurate spam identification. The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails, achieving an accuracy of 98.69%, AUC of 0.9981, F1 Score of 0.9724, and MCC of 0.9639. With a medium kernel size of (6, 9) and compact multi-window CNN architectures, it improves performance. Cross-validation illustrates stability and generalization across folds. By balancing high recall with minimal false positives, our method provides a reliable and scalable solution for current spam detection in advanced deep learning. By combining contextual embedding and a neural architecture, this study develops a security analysis method.