DaC-GANSAEBF: Divide and Conquer-Generative Adversarial Network—Squeeze and Excitation-Based Framework for Spam Email Identification

Email communication plays a crucial role in both personal and professional contexts; however, it is frequently compromised by the ongoing challenge of spam, which detracts from productivity and introduces considerable security risks. Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers, resulting in user dissatisfaction and potential data breaches. To address this issue, we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework (DaC-GANSAEBF), an innovative deep-learning model designed to identify spam emails. This framework incorporates cutting-edge technologies, such as Generative Adversarial Networks (GAN), Squeeze and Excitation (SAE) modules, and a newly formulated Light Dual Attention (LDA) mechanism, which effectively utilizes both global and local attention to discern intricate patterns within textual data. This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller, independently evaluated components. The model underwent training and validation using four publicly available benchmark datasets, achieving an impressive average accuracy of 98.87%, outperforming leading methods in the field. These findings underscore the resilience and scalability of DaC-GANSAEBF, positioning it as a viable solution for contemporary spam detection systems. The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.