Empowering machine learning for robust cyber-attack prevention in online retail: an integrative analysis

Kamran Razzaq (), Mahmood Shah, Mohammad Fattahi and Jing Tang
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Kamran Razzaq: The University of Northumbria Newcastle
Mahmood Shah: The University of Northumbria Newcastle
Mohammad Fattahi: The University of Northumbria Newcastle
Jing Tang: The University of Northumbria Newcastle

Palgrave Communications, 2025, vol. 12, issue 1, 1-15

Abstract: Abstract Cyber-attack prevention in online retailing has proven to be a challenging task. Machine learning (ML) algorithms play a significant role in preventing cyber-attacks. However, existing studies on ML-based prevention techniques are not well-researched. To bridge the gap in the literature, this article reviews existing literature on cybercrime prevention using ML techniques in an online retail context. A systematic literature review (SLR) was conducted of the literature on ML-driven cyber-attack prevention techniques in e-tailing, starting with 1828 relevant publications from four databases using the PRISMA approach. Finally, fifty-four journal articles from 2018 to 2023 were analysed. The review revealed that the research on ML prevention algorithms in e-tailing is an emerging field with a growing number of articles in recent years, and significant emphasis has been placed on supervised and unsupervised methods, with a particular focus on classification techniques, e.g., support vector machine and naive Bayes for prevention of cybercrimes in e-tailing. The SLR highlights several technical problems and offers suggestions for further study. Our research extends existing knowledge by synthesising existing literature and highlights cutting-edge findings and ML methods on cyber-attack prevention in online retailing. It also presents a holistic view of past research and offers the foundation for future studies. Furthermore, this SLR will help online retailers in knowledge provision and improve their ability to develop and implement ML practices to prevent cybercrimes.

Date: 2025
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DOI: 10.1057/s41599-025-04636-y

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