Machine Learning-Based Security for Cloud Computing Challenges and Implications
Cloud computing" is a computer model that provides end users with quantifiable, scalable, and on-demand services. These days, almost every organization uses computer technology extensively for infrastructure, cost-cutting, advanced platforms, data processing, data analytics, and other uses. End users can use any online application from anywhere to access the cloud service providers' (CSP) services. It is imperative to guarantee the security of cloud infrastructure, and a multitude of research employing various technologies is used to provide more accurate and potent defenses against cloud threats. Machine learning has shown to be a more useful technology in recent years for protecting cloud infrastructures. Machine learning algorithms are trained to generate models that mechanize the process of recognizing cloud threats more precisely than any other technology by using a range of actual datasets as training grounds. A couple of the most recent research that has worked on machine learning to defend against cloud attacks are examined in this paper.
Authors
- Leelkanth Dewangan
- Rushikesh Burle
- Rahul Dattangire (ORCID: https://orcid.org/0009-0002-6932-0865)
- Divya Biradar
Institutions
- The University of Texas at Arlington (US)
- ATA Engineering (United States) (US)
- Datta Meghe Institute of Higher Education and Research (IN)
- Raisoni Group of Institutions (IN)
Publication Details
- Published
- 2024-09-21
- DOI
- https://doi.org/10.1109/nkcon62728.2024.10774633
- Citations
- 2
- Primary Topic
- Network Security and Intrusion Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.70