Survey on Deep Learning Approaches for Detection of Email Security Threat

作     者：Mozamel M.Saeed Zaher Al Aghbari 

作者机构：Department of Computer SciencePrince Sattam bin Abdulaziz UniversityAl Kharj11912Saudi Arabia Department of Computer ScienceUniversity of SharjahSharjah27272United Arab Emirates 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第77卷第10期

页      面：325-348页

核心收录：

学科分类：0710[理学-生物学] 0401[教育学-教育学] 08[工学] 080203[工学-机械设计及理论] 0837[工学-安全科学与工程] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2023/R/1444) 

主　　题：Attackers deep learning methods e-mail security threats machine learning phishing 

摘      要：Emailing is among the cheapest and most easily accessible platforms,and covers every idea of the present century like banking,personal login database,academic information,invitation,marketing,advertisement,social engineering,model creation on cyber-based technologies,*** uncontrolled development and easy access to the internet are the reasons for the increased insecurity in email ***,this review paper aims to investigate deep learning approaches for detecting the threats associated with e-mail *** study compiles the literature related to the deep learning methodologies,which are applicable for providing safety in the field of cyber security of email in different *** data were extracted from different research *** paper discusses various solutions for handling these *** challenges and issues are also investigated for e-mail security threats including social engineering,malware,spam,and phishing in the existing solutions to identify the core current problem and set the road for future *** review analysis showed that communication media is the common platform for attackers to conduct fraudulent activities via spoofed e-mails and fake websites and this research has combined the merit and demerits of the deep learning approaches adaption in email security threat by the usage of models and *** study highlighted the contrasts of deep learning approaches in detecting email security *** review study has set criteria to include studies that deal with at least one of the six machine models in cyber security.