Open Access

REVIEW

Survey on Deep Learning Approaches for Detection of Email Security Threat

by Mozamel M. Saeed^1,*, Zaher Al Aghbari²

1 Department of Computer Science, Prince Sattam bin Abdulaziz University, Al Kharj, 11912, Saudi Arabia
2 Department of Computer Science, University of Sharjah, Sharjah, 27272, United Arab Emirates

* Corresponding Author: Mozamel M. Saeed. Email:

Computers, Materials & Continua 2023, 77(1), 325-348. https://doi.org/10.32604/cmc.2023.036894

Received 15 October 2022; Accepted 06 March 2023; Issue published 31 October 2023

Abstract

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, etc. The uncontrolled development and easy access to the internet are the reasons for the increased insecurity in email communication. Therefore, this review paper aims to investigate deep learning approaches for detecting the threats associated with e-mail security. This 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 organizations. Relevant data were extracted from different research depositories. The paper discusses various solutions for handling these threats. Different 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 studies. The 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 technologies. The study highlighted the contrasts of deep learning approaches in detecting email security threats. This review study has set criteria to include studies that deal with at least one of the six machine models in cyber security.

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Keywords

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