Preprint Article Version 1 This version is not peer-reviewed

Literature Review of Machine Learning and Threat Intelligence in Cloud Security

Version 1 : Received: 7 October 2024 / Approved: 7 October 2024 / Online: 8 October 2024 (08:51:21 CEST)

How to cite: Thaqi, R.; Krasniqi, B.; Mazrekaj, A.; Rexha, B. Literature Review of Machine Learning and Threat Intelligence in Cloud Security. Preprints 2024, 2024100512. https://doi.org/10.20944/preprints202410.0512.v1 Thaqi, R.; Krasniqi, B.; Mazrekaj, A.; Rexha, B. Literature Review of Machine Learning and Threat Intelligence in Cloud Security. Preprints 2024, 2024100512. https://doi.org/10.20944/preprints202410.0512.v1

Abstract

Cloud computing has transformed IT services by making them more scalable and cost-effective. However, this shift has also introduced new security challenges that traditional methods are finding hard to tackle. This review paper looks at how combining machine learning (ML) with dynamic threat intelligence can improve cloud security — an approach that hasn’t been widely explored yet. By reviewing recent studies, we show that ML and threat intelligence can do more than just detect known threats. They can also adapt to new and evolving ones, making cloud systems much secure against cyber attacks. Our analysis highlights how this combined approach provides better protection and flexibility. We also identify some important gaps in the current research and suggest areas for future study to make these security systems even more effective. This review aims to provide useful insights for researchers, helping to build more proactive cloud security strategies.

Keywords

Cloud computing; security; threat intelligence; machine learning

Subject

Computer Science and Mathematics, Security Systems

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