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

作　　者：Tawfeeq Shawly Ahmed A.Alsheikhy Yahia Said Shaaban M.Shaaban Husam Lahza Aws I.Abu Eid Abdulrahman Alzahrani 

机构地区：[1]Department of Electrical Engineering,Faculty of Engineering at Rabigh,King Abdulaziz University,Jeddah,21589,Saudi Arabia [2]Department of Electrical Engineering,College of Engineering,Northern Border University,Arar,91431,Saudi Arabia [3]Center for Scientific Research and Entrepreneurship,Northern Border University,Arar,73213,Saudi Arabia [4]Department of Information Technology,College of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia [5]Department of Information and Communication Technology,College of Computing Studies,Arab Open University,Kuwait Branch,Kuwait City,13033,Al-Safat,State of Kuwait [6]Department of Information Systems and Technology,College of Computer Science and Engineering,University of Jeddah,Jeddah,23218,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第3期3181-3212,共32页工程与科学中的计算机建模(英文)

基　　金：funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:71-829-2024).

摘　　要：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.

关 键 词：Email spam fraud light dual attention squeeze and excitation divide and conquer-generative adversarial network-squeeze and excitation-based framework security 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]