Machine Learning Approach for Cloud Computing Security
Garima Garima, Suhail Javed Quraishi · 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM) · 2022
The fast proliferation of Cloud Computing affects the way an industrial application is designed, delivered, and deployed. This acceptance of Cloud Computing has massive repercussions for the IT sector and associated industries since it does not only alter the customers' business models but also the other midfielders in the enterprise landscape.With the constant enhancement of the domain comes the responsibility to maintain the security of the cloud data and retreat ways to threats that may be encountered otherwise.This paper intends on discovering the potential security issues, threats, and attacks that individuals or an organization may experience while working on cloud or other distributed computing architecture and how it can be dealt with by employing technology like Machine Learning.We have explored numerous Machine learning approaches that are utilized to tackle cloud security challenges covering supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Whereupon, we evaluated the efficiency and draw a comparison for every approach based on its characteristics, benefits, and shortcomings. Furthermore, we highlight potential research paths to safeguard cloud frameworks.