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Business email compromise phishing detection based on machine learning : a systematic literature review
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Atlam, Hany F. and Oluwatimilehin, Olayonu (2022) Business email compromise phishing detection based on machine learning : a systematic literature review. Electronics, 12 (1). 42. doi:10.3390/electronics12010042 ISSN 2079-9292.
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Official URL: http://dx.doi.org/10.3390/electronics12010042
Abstract
he risk of cyberattacks against businesses has risen considerably, with Business Email Compromise (BEC) schemes taking the lead as one of the most common phishing attack methods. The daily evolution of this assault mechanism’s attack methods has shown a very high level of proficiency against organisations. Since the majority of BEC emails lack a payloader, they have become challenging for organisations to identify or detect using typical spam filtering and static feature extraction techniques. Hence, an efficient and effective BEC phishing detection approach is required to provide an effective solution to various organisations to protect against such attacks. This paper provides a systematic review and examination of the state of the art of BEC phishing detection techniques to provide a detailed understanding of the topic to allow researchers to identify the main principles of BEC phishing detection, the common Machine Learning (ML) algorithms used, the features used to detect BEC phishing, and the common datasets used. Based on the selected search strategy, 38 articles (of 950 articles) were chosen for closer examination. Out of these articles, the contributions of the selected articles were discussed and summarised to highlight their contributions as well as their limitations. In addition, the features of BEC phishing used for detection were provided, as well as the ML algorithms and datasets that were used in BEC phishing detection models were discussed. In the end, open issues and future research directions of BEC phishing detection based on ML were discussed.
Item Type: | Journal Article | ||||||
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Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HE Transportation and Communications H Social Sciences > HF Commerce |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
Library of Congress Subject Headings (LCSH): | Business communication, Interpersonal communication, Machine learning, Computer crimes -- Economic aspects, Corporations -- Security measures, Electronic mail systems -- Computer networks -- Security measures, Phishing, Electronic mail messages | ||||||
Journal or Publication Title: | Electronics | ||||||
Publisher: | MDPI | ||||||
ISSN: | 2079-9292 | ||||||
Official Date: | 22 December 2022 | ||||||
Dates: |
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Volume: | 12 | ||||||
Number: | 1 | ||||||
Article Number: | 42 | ||||||
DOI: | 10.3390/electronics12010042 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 22 January 2024 | ||||||
Date of first compliant Open Access: | 23 January 2024 |
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